JP5166539B2 - Recommendation generation system, apparatus, and method - Google Patents

Recommendation generation system, apparatus, and method Download PDF

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JP5166539B2
JP5166539B2 JP2010528043A JP2010528043A JP5166539B2 JP 5166539 B2 JP5166539 B2 JP 5166539B2 JP 2010528043 A JP2010528043 A JP 2010528043A JP 2010528043 A JP2010528043 A JP 2010528043A JP 5166539 B2 JP5166539 B2 JP 5166539B2
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recommendation
module
user
data
recommendations
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JP2011511968A (en
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オドノーグ、ヒュー
コリガン、シーン
クロウィー、シェイン
ペガム、アンドリュー
リリーホワイト、カート・デイビッド
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キシアム・テクノロジーズ・リミテッド
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Priority to US12/237,864 priority patent/US20090163183A1/en
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04MTELEPHONIC COMMUNICATION
    • H04M7/00Interconnection arrangements between switching centres
    • H04M7/0024Services and arrangements where telephone services are combined with data services
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04MTELEPHONIC COMMUNICATION
    • H04M2203/00Aspects of automatic or semi-automatic exchanges
    • H04M2203/65Aspects of automatic or semi-automatic exchanges related to applications where calls are combined with other types of communication
    • H04M2203/655Combination of telephone service and social networking

Description

35U. S. C. §119 priority claim This patent application is entitled “RECOMMENDATION GENERATION SYSTEMS, APPARATUS, AND METHODS” and is assigned to the assignee here And claims priority to provisional patent application No. 60 / 997,570 (filing date: October 4, 2007), which is expressly incorporated herein by reference.

  The present disclosure relates to mobile operating environments. More specifically, the present disclosure relates to providing an improved method of generating recommendations for users of mobile device carriers.

Mobile operators or mobile device carriers play a major role in today's telecommunications industry. Initially, the mobile operator was focused on generating revenue by expanding the subscriber base. However, it will now be understood that in some countries, the market has almost reached saturation and the scope of expansion of the subscriber base is very limited. As a result, mobile operators have expanded to provide added value to subscribers for the purpose of increasing their revenues.
One means of generating increased revenue is to sell premium services such as ringtones, wallpaper, Java games, etc. to users. These services can be provided by the mobile operator itself or by a business entity operating in partnership with the mobile operator to provide the service. These services may be made available for download to the user's mobile device upon payment of a fee.

  Many benefits, such as maximizing potential sales, occur based on recommending and promoting content or services to these users that may be of most interest to the users.

The following presents a simplified overview of these aspects for the purpose of enabling a basic understanding of one or more aspects. This summary of the invention does not provide an extensive overview of all contemplated aspects, but also identifies key or critical elements of all aspects and details the scope of any or all aspects. None of the explanations are intended. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a preparatory step to the form for carrying out the invention described below. To accomplish the above, the one or more aspects comprise the features described below and particularly emphasized in the claims. The following description and the annexed drawings set forth in detail certain illustrative aspects of the one or more aspects. However, these aspects represent only a few of the various ways in which the principles of the various aspects can be employed, and the described aspects include all these aspects and their equivalent aspects. It is intended to include.

  In one aspect, a method is provided for generating a recommendation for a user of a mobile device, wherein the user is associated with a service provider. A recommendation request is received. Data associated with the user and data regarding content available to the user are retrieved from the service provider. A recommendation list is generated based on the analysis of the retrieved user data, and the recommendation is generated by a plurality of different recommendation techniques.

  In another aspect, a computer program product having a computer-readable storage medium having instructions is provided. At least one instruction causes the computer to obtain a recommendation request. At least one instruction causes the computer to retrieve data associated with the user and data related to the content available to the user from the service provider. At least one instruction causes a computer to generate a recommendation list based on an analysis of the retrieved data, wherein the recommendation is generated by a plurality of different recommendation techniques.

  In an additional aspect, a system is provided for generating recommendations for a user of a mobile device, wherein the user is associated with a service provider. Means are provided for obtaining recommendations for recommendations. Means are provided for retrieving data associated with the user and data relating to the content available to the user from the service provider. Means are provided for generating a recommendation list based on an analysis of the retrieved data, wherein the recommendation is generated by a plurality of different recommendation techniques.

  In another additional aspect, a system for generating recommendations for a user of a mobile device is provided, wherein the user is associated with a service provider. The profile module stores and processes data associated with the user. The catalog module stores and processes content available to the user. A determination module communicates with the profile module and the catalog module, and generates a recommendation list for the user by analysis of data retrieved from the profile module and the catalog module, the recommendation comprising a plurality of individual Generated by the recommender module.

  In a further aspect, the method provides for generating promotions for a user of a mobile device. Access to attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices. A recommendation for the content to be offered is generated based on the attribute data, and a recommendation for the content to be offered is generated based on the behavior data. A subset of recommendations is selected by applying filtering restrictions. The subset of recommendations is transmitted to at least one subset of the plurality of mobile devices.

  In another further aspect, at least one processor generates a promotion for a user of the mobile device. The first module accesses attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices. The second module generates a recommendation for the content to be offered based on the attribute data and a recommendation for the content to be offered based on the behavior data. The third module selects a subset of recommendations by applying filtering restrictions. The fourth module sends the subset recommendations to at least one subset of the plurality of mobile devices.

  In a further additional aspect, the computer program product generates a promotion for a user of a mobile device. A computer-readable storage medium comprises instructions. The computer-readable storage medium includes at least one instruction for causing the computer to access attribute data and behavior data regarding a plurality of users of a corresponding plurality of mobile devices. The computer-readable storage medium generates a recommendation for the content to be offered based on the attribute data and generates a recommendation for the content to be offered based on the behavior data. And further comprising at least one instruction for causing the computer to perform. The computer readable storage medium further includes at least one instruction for causing the computer to select a subset of recommendations by applying filtering restrictions. The computer readable storage medium further includes at least one instruction for causing the computer to transmit the subset of recommendations to at least one subset of the plurality of mobile devices.

  In yet another additional aspect, the apparatus generates a promotion for a user of a mobile device. Means are provided for accessing attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices. Means are provided for generating a recommendation for the content to be offered based on the attribute data and for generating a recommendation for the content to be offered based on the behavior data. Means are provided for selecting a subset of recommendations by applying filtering restrictions. Means are provided for transmitting the subset of recommendations to at least one subset of the plurality of mobile devices.

  In a further aspect, the apparatus generates a promotion for a user of the mobile device. The profile storage component includes attribute data and behavior data regarding a plurality of users of a corresponding plurality of mobile devices. The profile and recommendation system generates recommendations for content to be offered based on accessed attribute data, generates recommendations for content to be offered based on accessed behavior data, and filters Select a subset of recommendations by applying restrictions. The network communication module transmits the subset of recommendations to at least one subset of the plurality of mobile devices.

1 is a block diagram of a mobile communication network profiling and recommendation system according to one aspect. FIG. FIG. 6 is a timing diagram of a profiling and recommending method according to one aspect. FIG. 2 is a block diagram illustrating an example wireless communication system incorporating the profile and recommendation system of this disclosure according to one aspect. FIG. 4 is a block diagram of a profile and recommendation system illustrating a more detailed view of components associated with a mobile operator interfacing with the profile and recommendation system, according to another aspect. FIG. 6 is a timing diagram for a method for profiling to interact with external network entities. FIG. 6 is a timing diagram for a method for exemplary interaction of a wireless device with a category page generated by a profiling and recommendation system, according to one aspect. 2 is a flow diagram of a method for accurately identifying content targets by understanding in detail subscriber usage and interests according to one aspect. FIG. 4 shows a block diagram of the main components of a catalog module, according to one aspect. FIG. 4 shows a block diagram of the main components of a profile module according to one aspect. 4 is a schematic diagram illustrating the four stages of a recommendation generation process in a decision module and a flow diagram of a method for generating a recommendation, according to one aspect. FIG. 6 shows a flow diagram of a method for summarizing main operations for generating recommendations according to yet another aspect. FIG. FIG. 11 shows a flowchart of a method of suboperation of the method of FIG. 10 according to yet another aspect. 12 shows a flowchart of a method of sub-operations related to the method of FIG. 11 according to yet another aspect. FIG. 6 shows a block diagram of a relationship between a recommender module and a decision controller, according to one aspect. FIG. 7 shows a flow diagram of a method for recommender processing according to one aspect. FIG. FIG. 6 illustrates a schematic example of various function calls in a decision recommender according to another aspect. FIG. 4 shows a block diagram of the main components of a decision module according to one aspect. FIG. 5 shows a block diagram of the main components of a network recommender according to one aspect. Fig. 5 shows a flow diagram for network recommendations according to one aspect. FIG. 6 is a schematic diagram showing how a decision module meets performance requirements using several different techniques, according to one aspect. FIG. 4 shows a block diagram of the main components of a promotional module according to one aspect. FIG. 6 shows a flow diagram of a method performed by a promotion module according to another aspect. FIG. 5 is a diagram illustrating details of additional modules of the profile and recommendation system of FIG. 4 according to yet another aspect. FIG. 5 illustrates a flowchart of a computing platform for performing at least a portion of a profile and recommendation method according to one aspect. FIG. 6 illustrates a network device having modules for performing a profile and recommendation method according to one aspect.

  Various aspects of this disclosure are further described below. It should be clear that the teachings herein can be embodied in a wide variety of forms and that any particular structure or function disclosed herein is merely representative. Based on the teachings herein, one of ordinary skill in the art should understand that the aspects disclosed herein can be implemented independently of the other aspects and that two or more of these aspects can be combined in various ways. For example, any number of the aspects described herein can be used to implement an apparatus or practice a method. Further, the apparatus may be implemented using other structures, functions, or structures and functions in addition to one or more of the aspects described herein, or structures, functions, or structures and functions other than the aforementioned aspects. Or can practice the method. As an example, many of the methods, devices, systems, and apparatus described herein are described with respect to providing dynamic mobile coupons in a mobile communication environment. Those skilled in the art should understand that similar techniques can be applied to other communication environments.

  As used in this disclosure, the term “content” refers to any type of application, multimedia file, image file, executeable, program, web page, script, document, Used to describe a presentation, message, data, metadata, or any other type of media or information.

  Whether the terms “component”, “system”, “module”, etc. used in this disclosure are hardware, software, running software, firmware, middleware, microcode, or a combination thereof. Regardless, it is intended to refer to an entity associated with a computer. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, an execution thread, a program, or a computer. One or more components can reside within a process or thread of execution, and the components can be localized on one computer and / or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. These components may be transmitted by other processes using local or remote processes, such as one or more data packets (e.g., interacting with other components in the local system or distributed system, or using signals over a network such as the Internet). Can communicate according to signals having data from components interacting with the system. Further, as will be appreciated by those skilled in the art, the components of the system described herein may be rearranged to facilitate achieving the various aspects, goals, advantages, etc. described in connection therewith. Or it can be supplemented by additional components and is not limited to the precise configuration shown in the given figures.

  Further, the various exemplary logic, logic blocks, modules, and circuits described in connection with the aspects disclosed herein are general purpose processors, digital signal processors, designed to perform the functions described herein. (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA), other programmable logic devices, discrete gate logic, discrete transistor logic, discrete hardware components, or any suitable combination thereof, Can be implemented or implemented together. A general purpose processor may be a microprocessor, but in the alternative, it may be any conventional processor, controller, microcontroller, or state machine. A processor may be a combination of computing devices, eg, a combination of a DSP and a single microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors associated with a DSP core, or any other suitable It can also be implemented as a combination with the configuration. Further, the at least one processor can comprise one or more modules operable to perform one or more of the operations or actions described herein.

  Furthermore, various aspects are described herein in connection with a mobile device. Mobile device, system, subscriber unit, subscriber station, mobile station, mobile, mobile device, cellular device, multimode device, remote station, remote terminal, access terminal, user terminal, user agent, user device, user equipment, And so on. A subscriber station can be a mobile phone, cordless phone, session initiation protocol (SIP) phone, wireless local loop (WLL) station, personal digital assistant (PDA), handheld device with wireless connectivity, or a wireless modem or processing device. There may be other processing devices connected to a similar mechanism that facilitates wireless communication.

  Moreover, various aspects or features described herein can be implemented as a manufacturing method, a manufacturing apparatus, or a manufactured product using standard programming or engineering techniques. Further, the operations or actions of the methods or algorithms described in connection with the aspects disclosed herein may be embodied directly in hardware, in software modules executed by a processor, or in a combination of the two. Is possible. Further, in some aspects, an operation or action of a method or algorithm is at least one of codes or instructions on a machine-readable medium or computer-readable medium that can be incorporated into a computer program product. It can reside as a set or as a combination of codes or instructions. Further, the term “article of manufacture” as used herein is intended to encompass a computer program accessible from a computer readable device, carrier, or media. For example, computer readable media include, but are not limited to, magnetic storage devices (eg, hard disks, floppy disks, magnetic strips) and optical disks (eg, compact disks (CD), digital versatiles). Disk (DVD)), smart cards, and flash memory devices (eg, cards, sticks, key drives). Additionally, various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The expression “machine-readable medium” can include, without limitation, wireless channels and various other media that can store, store, or carry instructions or data.

  In addition to the above, the expression “typical” is used herein to mean an example, instance, or illustration. Any aspect or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, the use of the expression typical is intended to specifically present a concept. Further, the expression “or” as used in this application and the appended claims is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless otherwise specified or apparent from the context, “X adopts A or B” is intended to mean any of the natural inclusive substitutions. That is, in this example, X can employ A, or X can employ B, or X can employ both A and B, and therefore , “X adopts A or B” is satisfied in any of the above cases. Further, the article “a” or “an” as used in this application and the appended claims is generally “1” unless otherwise specified or clear from the context that it is indicated to be singular. Should be interpreted to mean "more than one".

  As used herein, the expression “infer” or “inference” generally refers to the process of inferring or inferring the state of a system, environment, or user from a set of observations obtained through events or data. Point to. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states, for example. Inference can be probabilistic, i.e., calculating a probability distribution over the subject state based on data and event considerations. Inference can also refer to techniques employed for assembling higher level events from a set of events or data. As a result of the inference, whether new events or actions will be constructed from a set of observed events or stored event data, and whether these events are interrelated in time And whether these events and data are from one or several events and data sources.

  With reference to FIG. 1, these aspects are illustrated in which a mobile operator 12 of a wireless communication network 14 and its business partner, described as a content provider 16, take up content and service uptake and a mobile device 18 of a subscriber 20. A profile and recommendation system 10 is provided that allows advance promotion to the subscriber base shown as. In one example, this is accomplished by generating a recommendation list 21 that is individually optimized for a particular subscriber 20 for delivery to the mobile device 18. These recommendations can be displayed on a portal associated with the mobile operator or delivered to the mobile device, for example, by mobile messaging.

  According to one aspect, the stored profile data 22 comprises attribute data 24 or behavior data 26. A plurality of corresponding recommenders, described as attribute recommender 28 and behavior recommender 30, associate each data 24, 26 with a content feature cross-reference 32 of catalog index 34 of content 36. The provisional recommendations from the recommenders 28, 30 have the confidence level assigned by the confidence weighting component 38. For example, weak or strong associations can be determined. As another example, an attribute or action can be determined to be weakly related through speculative analysis of limited events, or can be determined to be strongly related through explicit input or repeated actions. The weighted provisional recommendations can be sorted by the sorting component 40.

  Before or after sorting, filtering component 42 implements exclusions 44 to avoid inappropriate recommendations. Exclusions can be explicitly specified by the subscriber 20 as shown at 46, for example, to limit certain categories of recommendations that can be rejected. The exclusion can be specified by the mobile operator 12, as shown at 48, for example, specifying a computing platform target suitable for content (eg, an audio file suitable for a mobile device having an MP3 media player). Exclusions can also be traced from profile data 22, as shown at 50, for example, purchases of content that would otherwise be recommended again or recommendations that are repeatedly ignored by the subscriber 20. is there. Exclusions can also be derived from the content provider 16, which can be the mobile operator 12, by providing device or software configuration compatibility information 52. This excludes mobile devices 18 that cannot use the recommended content successfully.

  Recommendations are generated by analyzing the subscriber information available to mobile operators in relation to the offered content and services to determine the content and services that are most likely to be of interest to the subscriber. . In particular, the profile and recommendation system 10 delivers recommendations to the subscriber 20 at a time when the subscriber 20 is determined to be most likely to purchase based on an attribute or behavioral assessment as an individual or group member. Also make it possible. Profiles and recommendation systems are suitable for generating promotions when it is desired to actively promote specific content or services to the subscriber base.

  In FIG. 2, a method 50 for profiled recommendations for content to offer on a mobile device according to one aspect is shown. At block 52, the available content is cataloged and features are described. At block 54, user attributes and behavior data are maintained. At block 56, an association can be made for a user having the attribute based on a peer-to-peer (P2P) relationship with the user whose attribute was previously determined. This indirect association can have a lower weight than an association made from explicit or direct information. At block 58, the user may be associated with a group, such as explicit membership, frequent access to a portal for the group, and so on. This associated group can have attribute and behavioral data that can be used for the associated user, particularly when insufficient data is received for the associated user. At block 60, a recommendation request is received. In block 62, a recommendation request to obtain an item for the subscriber or a subscriber to the item is generated for each attribute data. At block 64, a recommendation request to obtain an item for the subscriber or a subscriber for the item is generated for each behavioral data. The recommendations are weighted by assigning a confidence level to each (block 66). The weighted recommendations are sorted so that the best subset can be delivered (block 67). Exclusions are accessed, eg, previous purchases, tracked previous offers, user settings (restrictions), operator instructions, device suitability / configuration, etc. (block 68). At block 70, recommendations are filtered by accessing applicable exclusions. At block 72, the filtered and sorted recommendations are sent for presentation on the mobile device. At block 74, confirmation regarding the recommendation presentation and selection by the user is received. The tracking is updated with this data (block 76).

  Referring now to FIG. 3, according to one aspect, a block diagram illustrating an exemplary wireless communication system 100 that incorporates the profile and recommendation system 101 of this disclosure is provided. The wireless communication system 100 may include one or more wireless devices 102 that communicate with a base station controller (BSC) 104 through a wireless network 103. The wireless device 102 can be of any suitable type and includes, for example, a handheld PC, PDA, or mobile phone. The base station controller 104 communicates with a mobile operator's communication infrastructure, eg, a wireless gateway, shown as a wireless application protocol (WAP) gateway 105, a multimedia message switching center (MMSC) 106, and a short message switching center (SMSC) 107. . The mobile operator's system also includes a server 108 configured to function as a portal.

  In one example, the profile and recommendation system 101 of this disclosure is communicatively linked to a mobile operator's communication infrastructure. In one example, the profile and recommendation system can provide mobile operators and associated business partners by providing a unified view and advanced profiling of subscriber base and intelligent recommendations, as described in more detail below. Enables the advancement of service uptake.

  In accordance with one aspect, FIG. 4 illustrates an exemplary block diagram of a profile and recommendation network 200 showing interaction between certain components associated with the mobile operator 202 and profile and recommendation system 101 of this disclosure. These systems can be integrated directly within the mobile operator's communications infrastructure 206 or, alternatively, can be part of a business partner's system associated with the mobile operator. Infrastructure 206 may include service and content information component 208, subscriber profile information source 210, and recommendation application 212 used by administrator 213. The profile and recommendation system 101 interfaces with a content delivery system 214, which includes a WAP gateway 105, a short message service center (SMSC) 107, and a multimedia message transmission service center (MMSC) 106. And can communicate with the wireless device 102. The content delivery system 214 provides content delivery capabilities through connections to network systems such as the WAP gateway 105, the SMSC 107, the MMSC 106, and the like. This enables the profile and recommendation system 101 to deliver and receive any type of mobile content or service to the user or subscriber 222 of the wireless device 102 in communication with the content delivery system 214. This capability is achieved when the profile and recommendation system 101 is used to deliver promotional information (eg, via SMS, MMS, WAP Push, etc.) and when the profile and recommendation system 101 completes content delivery (eg, , Chord ringtones, wallpaper, games, etc.).

  The service and content information component 208 can comprise an external platform, such as a value added service (VAS) or portal 226, with which the profile and recommendation system 101 can communicate. In one example, integration with VAS platform 226 can facilitate creation of a complete catalog of content available to mobile subscribers 222 of one or more wireless devices 102. This allows the profile and recommendation system 101 to more intelligently retail available content or services offered by the mobile operator or its partners. Integration with portal 226 allows for the delivery of targeted promotions to users or subscribers 222 using portal 226, and these users or subscriptions for later reference from subscriber profile information source 210. The acquisition of the information component 228 regarding the person's behavior is enabled. In one example, the subscriber profile information 228 includes call data, gender, date of birth, previous purchase history, interested or uninteresting display, spending pattern, mobile device type, current geographic location, frequency of calls. Or one or more of other metadata.

  FIG. 4 further illustrates details of exemplary main components of the profile and recommendation system 101 according to one aspect. These include a catalog module 230, a profile module 232, a determination module 234, and a promotion module 236. Each of these modules is described in further detail below. The catalog module 230 allows the profile and recommendation system 101 to be utilized as a central catalog for large volumes of content or services. In this way, a more detailed overview of available content / services can be provided to other systems (eg, portals), thereby allowing better management of the content retailing process.

  By way of example, an operator catalog 238 maintained by a mobile operator at a centralized location can include a complete catalog of voice, data, and other services provided by the operator. In one example, the catalog module 230 can maintain the product ID code and structure 240 defined in the mobile operator's central catalog 238.

  In use, as shown in FIG. 5A, in one example, the method 300 for profiling and recommendation interacts with an external network entity shown as a subscriber profile information source 302. On the other hand, the subscriber profile information communicates with the mobile operator's existing billing system 304 and customer relationship management (CRM) system 306. The profile and recommendation system 308 performs billing integration when it is responsible for content delivery to the user or subscriber wireless device 312.

  In another aspect shown in FIG. 5B, the method 358 illustrates the wireless device 360 accessing the wireless gateway 362 to receive recommendations on the category page generated by the profile and recommendation system 364. As shown at 366, the wireless device 360 requests the wireless gateway 362 to view the category page, and the wireless gateway 362 forwards the recommendation request to the profile and recommendation system 364, as shown at 368. . The recommendation is generated and returned to the wireless gateway 362, as shown at 370, and the wireless gateway 362 displays the recommendation on the category page at the wireless device 360, as shown at 372. When the user selects a recommended item on the category page, this selection is communicated to the wireless gateway 362 as shown at 374. Wireless gateway 362 provides browsing activity feedback to profile and recommendation system 364 as shown at 376. A user of the wireless device 360 may request additional viewing of category pages communicated to the wireless gateway 362 as shown at 378. As shown at 380, the wireless gateway 362 requests a recommendation to populate the category page and, as shown at 382, a new recommendation for the category page that reflects the latest browsing activity. For example, by deleting an item purchased or offered more than a specified threshold number of times or adding an item associated with a selected recommended item.

  Referring back to FIG. 5A, the profile and recommendation system 308 can support a range of billing scenarios as an application programming interface (API) to a specific customer billing system 304. The wireless device 312 sends a real-time information request to the billing system 304 as shown at 320 to make a real-time balance inquiry, notify purchase events, eg, new subscriptions, and utilize promotional balances. (E.g., subscribers can download two games per month free of charge for the first three months of a new subscription). The billing system 304 returns information or transaction confirmation to the wireless device 312 as indicated at 324. The real-time nature of this API performed by the profile and recommendation system 308 ensures that prepaid subscriber balances are effectively managed and revenue loss is minimized.

  By way of example, as shown in block 328, the profile and recommendation system 308 can use the charge code in generating call data records (CDRs) for billing purposes, and these call data records (CDRs) are , 330 is sent to the billing system 304. These codes are defined by the user and can be associated with all content from the content source. In one aspect, the interface can support output of multiple CDR formats. In addition to the basic charge code information, a description of the actual content that can be included in the subscriber's bill can also be included. It can also manage promotional information billing tags with zero or special ratings. According to one implementation, as shown in block 332, the profile and recommendation system 308 processes payment data that helps the mobile operator to differentiate between the actual content and the pricing associated with the bandwidth used to deliver the content. It can also be sent to billing system 304 as shown at 334. It can also distinguish between wholesale prices and retail prices to report the amount that mobile operators incur to content providers.

  The profile and recommendation system 308 is further operable to allow multiple agents to feed data into the profile module 232 (FIG. 4) or the catalog module 230 (FIG. 4). For example, a user or subscriber purchase history from the billing system 304 as one agent 336 receives user or subscriber attribute information from the CRM system 306 as shown at 338 and another agent 340 as shown at 342. Can be configured to receive.

  The recommendation application 212 operates to provide a means for allowing the profile and recommendation system 308 to interface with external applications, through which all aspects of the profile and recommendation system 308, for example, It is possible to manage sales promotions, catalogs and content management. In one aspect, this can be a web-based user interface (not shown) available to the administrator, who can be a mobile operator employee or an associated business partner, such as a content partner, Can be. Many features of this recommendation application 344 will be understood when described below with reference to the various modules of the profile and recommendation system 308.

  During the configurable time period, as shown at 346, a copy with a managed version of the content catalog can be exported to the central catalog storage location 348. According to one aspect, the profile and recommendation system 308 maintains data about content in the central merchandise catalog at block 350 so that the profile and recommendation system 308 centralizes the XML format of the catalog module 230 as shown at 352. Allows export to catalog storage location 348. In this way, by way of example, all details are preserved and no problems with content re-indexing occur.

  Referring to FIG. 6, according to one implementation, a method 400 for profiling users or subscribers provides a detailed understanding of subscriber usage and interest and a detailed content catalog (built through profile module 232 (FIG. 4)). It will be easy through. This detailed content catalog is made possible through the definition of metadata. In one example, the catalog module 230 (FIG. 4) operates to create a list of all content or services available across mobile operators and associated business partners (block 402). Further, the catalog module 320 can operate to identify content or services supported by each device (block 404). Catalog module 320 may operate to further identify the relationship between content and services (block 406). In addition, the catalog module may operate to associate a subscriber segment with each content or service (block 408). The catalog module 230 provides detailed target specific information regarding which subscriber segment each content or service applies to, the relationship between the content or service, and which devices support the content or service and usage information (block 410). ) To work further.

  According to an example, catalog module 230 provides a basic set of metadata definitions (block 412) and defines an additional infinite range of metadata for individual content or services (block 414). The wider the range of metadata elements defined for a content or service item, the more options for classifying the content or service (into asset groups) and for mapping the content or service to users or subscribers with diverse interests Will increase. By one non-limiting example, this metadata includes, among other things, data volume, pricing information (retail and payment). Adult content, promotional tags, content location (stored locally or remotely) can be specified.

  The catalog module 230 can be implemented in situations where content or service metadata is stored, or when additionally storing some or all of the content or service itself (eg, ringtone, wallpaper, etc.). In the latter case, a content module may be deployed that operates to manage and distribute content that is stored locally (block 416). In one exemplary deployment where the content itself is not stored, the catalog module 230 operates to maintain one or more links to where the content can be found (block 418). In any case, the timely and accurate population of content metadata allows the profile and recommendation system 101 module (FIG. 4) to function effectively.

  FIG. 7 shows a block diagram of the main components of the catalog module 230 according to one aspect. These include a content group classification module 501, a search module 502, a content management module 503, a content database module 504, and a content capture module 505. The function of each of these modules is described in further detail below.

  In one aspect, the content group classification module 501 can provide asset group classification to the portal. Asset group classification allows to build a page dedicated to content themes and user or subscriber fees and mobile device capabilities. The catalog module 230 can automatically integrate all content or services from multiple sources (eg, films, single asset groups, etc.). In another example, there may be an asset group for a female pop singer to which the Britney Spears ringtone belongs. Furthermore, there can be an asset group for Britney Spears to which the same ringtone belongs, while the same ringtone can belong to an asset group for the top 10 ringtones. Asset group construction is based on the group classification for metadata and the range of asset groups to which a piece of content belongs. In one aspect, the construction of asset groups for content items by the content group classification module 501 can be limited by the number of metadata tags defined for that content item.

  According to one aspect, the search module 502 provides an embedded search engine that provides keyword-based content search capabilities. This engine works by building a search index from content metadata. It is possible to construct an exact metadata field that can be searched. For example, the search module 502 enables a search using commonly used search fields such as artist and title and other fields such as cost. The search index is updated from the catalog data in a configurable period.

  In one aspect, the capability of a mobile device is an abstract mapping between a content category and the devices that support that content. For example, a device capability of “MMS30K” can be used to track devices that support MMS messages up to the 30 Kb limit. In one implementation, the profile and recommendation system 101 can operate to track which devices 102 support this capability and which content items require this capability. The catalog module 230 can now be queried by the search module 502 for content supported by a particular device. In one example, the latter can be achieved by a portal. In this way, users or subscribers are not offered content that is not compatible with their respective mobile devices. Furthermore, a device capability of “MMS 100K” can exist. In one example, a device can have both capabilities and can prefer the most appropriate capability, in this case the 100K version. According to one aspect, content information is retrieved by the content capture module 505. When retrieving information about a particular user or subscriber, the profile and recommendation system 101 returns content that is most appropriate for that user or subscriber's device.

  The profile and recommendation system 101, when deployed with the profile module 232, can track which content is most popular within a certain category. This information can be reported to the portal for the generation of categories such as “Top 10 Games” or “Top 10 Arcade Games”. If usage information is not available from the profile module 232, the catalog module 230 can have an explicitly specified content popularity rank. This can be obtained from an external system having this information, or can be manually set by the administrator 213. In one example, the content of catalog module 230 is added to content database module 504. This can be stored by communication between the catalog module 230 and the service and content information block 208 (eg, portal, etc.) for retrieval of available content or services. The recommendation application 212 includes metadata associated with content that an administrator 213 in the form of an authorized user (for example, a content provider) adds to the catalog module 230 through communication with the content management module 503 by the content provider. Makes it possible to manage. According to one example, an XML API is also provided that allows mass upload of content updates. Through these interfaces, the administrator 213 can build information about the content, such as the storage location of the content (eg, local, remote, etc.), the price that the content provider charges for the content, etc. According to one example, a content provider can provide a basic promotion that allows the content provider to specify a discount method for discounting the price of content or services to mobile operators, users or subscribers. It is also possible to build things. The interface can also provide real-time statistics on content usage and content delivery status, and subscriber activity.

  When uploading catalog data to the content database module 504, in one example, a catalog zone can be specified. If specified, the catalog zone is used to divide catalog data into different partitions. In one example, a catalog zone can be used to divide content items into different areas so that recommendations for specific subsections of content are returned. For example, it is possible to assign a catalog zone “music” to all music content sources and a catalog zone “game” to all game content sources. When requesting recommendations, a specific catalog zone can be specified to ensure that only recommended items are returned from that catalog zone (ie, by specifying a “music” catalog zone) Get recommendations only, or get recommendations for games by specifying a catalog zone for "Games"). In one example, not specifying a catalog zone will return recommendations from all catalog zones (eg, “game” and “music” in this example).

  With continued reference to FIG. 4, the profile module 232 may include user or subscriber online behavior, segmentation, interest, for example, to allow one-on-one identification of promotions and services. Provides an active mechanism for profiling possible spending patterns, devices, birthdays, anniversaries, peak usage periods, etc. Profile module 232 provides the data required for decision module 234 to make its intelligent recommendations. In one example, the richer and more appropriate the data contained in the profile module 232, the better the recommendation.

  In another aspect, in FIG. 8, the block diagram of the main components of the profile module 232 comprises a profile database module 601, a profile management module 602, a profile group classification module 603, and a profile capture module 604. In the following, the function of each of these modules will be described in more detail.

  By way of example, by default, the profile module 232 obtains many of the most common details about a user or subscriber, both in terms of actual subscriber attributes (eg, prepaid or postpaid) and purchase history information. It is possible to include the required metadata for the purpose. Profile module 232 may further provide the ability to easily extend the default subscriber profile data model to meet specific needs.

  Still referring to FIGS. 4 and 8, in one example, the administrator 213 allows the recommendation application 212 to communicate with the profile management module 602 to determine the new metadata fields, each data type, and these new metadata fields. It is possible to define whether it is a free format or a field from a fixed list and whether these new metadata fields are mandatory fields. The new metadata can now be used in the same way as the default metadata in areas such as profile groups, decisions, profile data import and export. In this way, the administrator 213 can optimize the profile information to better suit the usage of the system desired by the administrator 213 and the process of importing / exporting profile data from external systems with existing metadata. Can do.

  With particular reference to FIG. 8, the profile group classification module 603 further provides functions for generating and managing profile groups based on information stored in the profile database module 601. In one example, these groups comprise users or subscribers with common attributes or purchasing behavior. In one example, the group can be constructed by one or more of the following mechanisms. (A) Import a user or subscriber list from a subscriber profile information source, such as a CRM system. This can be a manual file import process or can be automated via a connection module, (B) by user or specific profile metadata (eg, all men, prepaid subscribers, etc.) Categorize subscribers, (C) analyze past usage of the system (eg, subscribers who have purchased or have not purchased a particular content or category of content), and (D) Categorize subscribers by type of device they own (eg, MMS compatible devices, etc.). By way of example, a group can be statically defined with respect to a specific time period (eg, a user or subscriber who purchased a game in December 2004, etc.) or can be dynamic (eg, last month). Users or subscribers who have not purchased ringtones, etc.).

  With continued reference to FIG. 4, in one example, profile groups can serve in the following manner. That is, (A) an online promotion that promotion module 236 can use profile groups to determine which subscribers should see a particular banner ad when the subscriber visits the portal site. . For example, it is possible to build a subscriber group that includes all subscribers with SMS alerts and MMS-enabled mobile devices 102. This is because (B) the promotion module 236, which is used to offer MMS alert services to these users or subscribers 222 the next time these users or subscribers 222 visit the portal site 226, Outbound promotions that can use profile groups to determine whether outbound promotions should be sent to users or subscribers 222 via SMS, MMS, WAP Push, etc. For example, it is possible to build a subscriber group that includes all users or subscribers 222 who have purchased “FIFA 2004”. This is used to perform outbound promotions for “FIFA 2005”, (C) Profile group details to update a centralized system (eg, subscriber profile information source 210 such as CRM or data warehouse) Can be exported to an external system, thus assisting the process of maintaining an integrated subscriber database (not shown), and (D) profile groups execute target groups Can be used in connection with promotional campaigns to select users or subscribers 222 that receive information about new promotions or services. In one example, the promotion module 236 operates to automatically manage user or subscriber selection or exclusion.

  In one example, it is possible to maintain a count of the number of contacts per subscriber and the contact method preferred by the user or subscriber. By way of example, the latter may be required for regulatory reasons or based on the mobile operator's customer contact policy. This can be used to prevent a large amount of promotions from being sent to users or subscribers with a wide range of interests within a particular time frame. Recording preferred contact methods can allow promotions to be directed to users or subscribers in a way that is most likely to elicit a positive response.

  According to one aspect, one of the functions of the profile module 232 is to efficiently store user or subscriber attributes and their purchase history in the profile database module 601 for later retrieval by the profile capture module 604 when requested. Is to store. In one example, the storage mechanism is configured to be able to store large amounts of data in a manner that enables high performance data updates and data access. In one aspect, it is possible to use an Oracle relational database for storage of all platform data including profile data. In another example, a dedicated but lightweight, data access layer that uses native Oracle connection APIs (eg, Java Database Connection (JDBC), Oracle Call Interface (OCI), etc.) efficiently connects to the database. . In one example, techniques such as performing a structured query language (SQL) statement that performs operations through multiple database connections, and intelligent data caching may be used. The data access layer can encapsulate specific storage mechanisms from other parts of the platform and provide a clean level for building profile functions.

  Further, in one example, the extensible metadata feature of profile module 232 may require that profile database module 601 be able to manage these metadata definitions and their associated values. This capability can be provided through a metadata log library that allows any system entity to be used to define new metadata attributes. In one example, the latter is used in connection with profile module 232 and catalog module 230.

  According to one aspect, profile data can be obtained from external systems (eg, subscriber profiles and information source blocks) or from information constructed by the profile and recommendation system 101. Data from the external system can be sent into the profile database module 601 via a connection module (not shown in FIG. 4). In one aspect, the data can be associated with user or subscriber attributes and can include usage information when available.

  In one implementation, the degree to which the profile and recommendation system 101 can store profile information independently can depend on the modules deployed and the manner in which these modules are used. For example, if content modules are used to deliver certain content or services, usage information regarding these services can be automatically recorded in the profile database module 601. The profile and recommendation system 101 (FIG. 4) may provide the service and content information component 208 (eg, portal 226) such that the service and content information block reports the purchase of the user or subscriber to the profile and recommendation system 101. , Etc.), the information can also be recorded.

  In one example, when uploading profile data to the profile database module 601, a profile zone can be assigned to the data. If specified, according to one example, the profile zone can be used to divide user or subscriber transaction data into different partitions. By way of example, profile zones can be used to divide subscriber transactions into different partitions so that recommendations can be made using data from a particular partition. For example, if transaction data from two operators (eg, Mobile Virtual Network Operator (MVNO)) is uploaded to the system, a different profile zone value can be given to transactions from each operator.

  With reference to FIG. 9, an example illustrates the case where the recommendation generation method 700 has an offline stage I that performs preparation (block 702) that includes total data generation. The next three real-time processes include stage II where the selection for the individual is made (block 704). Stage III performs weighting for marketing based on insight (block 706). Stage IV performs filtering for rule-based presentation (block 708).

  With continued reference to FIGS. 4 and 8, during the phase I “preparation” process, the decision module 234 generates data for each profile zone individually, and data for a default zone that combines all data. When requesting a recommendation, the requested profile zone can be selected in the user interface (UI) or specified when calling the API. If a profile zone is not specified when requesting recommendations by profile capture module 604, data from the default zone can be used.

  The decision module 234 is used to recommend the best offer to the user or subscriber. According to one implementation, the latter differs from the promotion module 236, where the administrator 213 best offer selection mechanism uses a fixed number of predefined promotions and several predefined profile groups. An example using decision module 234 can automatically generate an optimal offer without manual configuration.

  With continued reference to FIG. 4, by utilizing the decision module 234 and a unified view of subscribers and available content and data services, a direct intelligent of optimal content or services for individual users or subscribers. Can be matched and used wireless device 102, demographic figures for user or subscriber, previous purchase behavior, previous purchase behavior of user or subscriber and other similar subscriber purchase habits Factors such as relevance, available funds, etc. This unique and extensive decision criterion ensures that only relevant content is offered or presented to the user or subscriber 222.

  The determination module 234 further utilizes the merchandise information available from the catalog module 230 along with the subscriber information available from the profile module 232 to generate recommendations. According to one aspect, the more information that these modules are available, the better recommendations can be generated by the decision module 234.

  According to one aspect, the determination module 234 can perform substantially all of the collection by the catalog module 230 and the profile module 232 to present appropriate, interesting and timely content or services and promotions to the user or subscriber 222. Use the information. Accordingly, the decision module 234 provides a self-learning capability that allows the mobile operator to reliably maximize sales opportunities substantially automatically each time a user or subscriber 222 uses the mobile operator's content or service. To do.

  In one or more non-limiting aspects, the determination module 234 has several use cases. (A) for a subscriber when the subscriber accesses the portal and the profile and recommendation system 101 is asked to propose an optimal promotion (if defined by the promotion module 236) (B) the content (stored in the catalog module 230) for the user or subscriber, instead of being selected by a promotion for which one content has been explicitly generated, Service selection. The latter eliminates the need for the administrator 213 to explicitly generate a promotion when an appropriately described catalog is available, (C) the best user or subscription to identify as a promotion target Selection. The latter is done when selecting a user or subscriber target list for outbound promotions. In one example, the determination module 234 can identify a user or subscriber that the determination module 234 determines with the least certainty when actively responding to a promotion, (D) a user or Selection of the best offer to make to a group of subscribers, where the content or service to be offered is selected from the catalog module 230 rather than a specific promotion. In one example, the decision module 234 identifies the best content item or service to offer to each user or subscriber 222 starting from the identified group of users or subscribers 222 and confirms the positive (E) Cross-selling content or services based on items that have already been purchased, selecting content items that have a probability of deriving a positive response above a specified minimum value. The decision module 234 can use information about the subscriber's last purchase to identify other content items or services that should also be recommended for that user or subscriber 222. The content or service can be recommended to the user or subscriber 222 at the portal immediately after purchase or via automated outbound promotions.

  With further reference to FIG. 9, according to one aspect, a schematic diagram illustrating four stages of a recommendation generation method 700 in a decision module is provided. The four stages are stage I-preparation 702, stage II-selection 704, stage III-weighting 706, and stage IV-filtering 708.

  During stage I (preparation portion 702 of method 700), the data accumulated in the profile and recommendation system 101 and its associated business partners is reviewed (block 710) to determine general behavior trends, relevance, and A pattern is calculated (block 712). In one example, the stage I preparation method 702 performs data accumulation at the overall level as opposed to individual levels (block 714). Stage I preparation method 702 may be performed periodically in an offline / background process (block 716). The frequency with which stage I 702 is executed may depend on the frequency with which the data is updated. Since each decision recommender (algorithm) has its own preparation stage, the decision recommender can be optimized to run at a frequency that suits each recommender. In the following, additional information regarding each decision recommender is provided with respect to FIGS. According to one illustrative example, output to the respective database is performed (block 718). According to another example, data can be stored in the profile module and catalog module 232 and 230, respectively, from which input items are derived. One skilled in the art should understand that data can be accumulated from other suitable sources in different aspects.

  During the stage II selection portion 704 of the method 700, the decision module 234 can access specific information about the individual (eg, demographic figures, past purchases, preferences, etc.) (block 720). ), The decision module recommendation algorithm selects recommendations for individual users or subscribers (block 722). In one example, the decision module 234 can generate a large (deep) list of recommendations ordered by confidence levels (block 724).

  During the stage III weighting method 706, the decision module 234 provides the administrator 213, content provider, ability to specify items to be prioritized / removed in terms of likelihood recommended to the user or subscriber. Or it can be provided to a mobile operator (block 728). In one exemplary use case, it may be desirable to promote certain content or services at certain times (eg, Christmas) or promote certain major artists (block 730). During stage III 706, the weighting information is used to change the order of the subscriber's recommendation list before proceeding to the next stage (block 732).

  During stage IV-filtering 708, the decision module 234 can retrieve the recommendation list generated in stage II and filter based on past purchases and device suitability (block 733), which in the calling application The result is made more specific for a particular context (block 734). For example, it may be desirable to return only recommendations regarding a specific artist, content type, genre, or cost (block 736). In addition, items that have already been recommended a certain number of times for the user or subscriber may be filtered out (block 738). According to an example, filtering criteria are defined by the calling application as part of an API call that is described to occur before block 734 (block 740). Define the system-wide filtering criteria, eg, the maximum number of times any item should be recommended for any individual (block 741), as shown in subsequent block 738, after which the subscriber The recommendation tracking may be updated to count the number of recommendation offers in the future (block 742).

  According to one aspect, stage I 702 does not work at the individual subscriber level, while stages II 704, III 706, and IV 708 use stage II 704 data to generate targeted recommendations. , III 706, and IV 708 function at the individual subscriber level. That is, as shown in FIG. 8, individual user or subscriber profile data 752, user or subscriber attributes (eg, age, segment, etc.) 754, and user or subscriber history information (eg, purchases) , Etc.) stored profile data 750 is utilized. Profile data 750 may also comprise user / subscriber input / feedback 758. Further, the determination module 234 can automatically filter the results using certain specific subscriber information, as shown as a preference filter 760. According to one example, a typical filter 760 is as follows. That is, (A) the subscriber's mobile device manufacturer and model 762-when the information is specified, the decision module operates such that only content or services that are operable on the subscriber's device are recommended ( B) Previous purchase block 764-determination module 234 operates such that items that the subscriber has already purchased are not recommended for that subscriber, (C) previous negative feedback 766-determination module 234 (D) Restricted Content 768—Indicates that the item is a restricted item, which operates to prevent items that the subscriber has already given negative feedback / ranking information from being recommended to that subscriber Yes, the item allows subscribers to receive the content (eg adult content) Which means that should not be recommended 該品 eyes as long as you have not selected in explicit. It should be noted that the determination module 234 can use real-time information when generating recommendations. For example, if a user or subscriber purchases or browses an item on a portal, this can immediately affect the recommendations presented to that user or subscriber. In the example, the real-time event may be provided to the profile and recommendation system 101 via the appropriate API call rather than via the profile module 232 API. Alternatively, the profile API can be used only for sending purchase information.

  FIG. 10 illustrates a method 800 that summarizes the main operations in a process flow for generating a recommendation, according to one aspect. At 802, an external (calling) application requests recommendations for the subscriber from the profile and recommendation system, which can include passing context or other parameters. At 804, data stored during the preparation phase (previously described with respect to FIG. 9) associated with a particular subscriber is retrieved. At 806, a decision module generates a plurality of recommendations for the subscriber based on the retrieved data. At 808, the recommendations generated at 806 are screened to provide a final recommendation list for the subscriber. At 810, this final recommendation list is returned to the external application. In one example, the external application is online, via a portal, outbound, via SMS, MMS, WAP push messages, or for delivery to users or subscribers via any suitable mechanism. The generated recommendations can be passed to the user's or subscriber's mobile device.

  Referring to FIG. 11, by way of example, a process flow that further includes sub-operations of operations 804 through 808 of the process flow of FIG. At 902, data from a data module associated with a user or subscriber is retrieved, where the data module was constructed from a periodic examination of stored data associated with the user or subscriber. At 904, this data is input into a plurality of recommendation algorithms, each of which recommends a recommendation for the subscriber in real time. The resulting recommendations are combined to form a deep recommendation list. At 906, the deep recommendation list is ordered by the confidence level. At 908, the recommendation list is reordered based on the weighting rules. At 910, further processing is performed on the reordered list to generate a final recommendation list.

  FIG. 12 shows a process flow of sub-operations related to operation 910 of FIG. 11 according to one implementation. At 1002, the weighted recommendation list is filtered to make it closer to the context of the external application requesting the recommendation (as described above in stage IV). At 1004, recommendations regarding content or services that have been previously purchased by the user or subscriber are removed from the filtered list. At 1006, recommendations that are not compatible with the user's or subscriber's mobile device are also excluded. This establishes a final list for a particular user or subscriber. It should be noted that the order of operations 1002, 1004, and 1006 in FIG. 12 is interchangeable. As previously shown and described, filtering can further include limited content and items that have already been recommended a very large number of times.

  As described above, by way of example, the determination module 234 can use a combination of different algorithms to generate the substantially best recommendation possible. Using multiple algorithms allows the decision module 234 to utilize a technique that is substantially optimal with respect to the quality and quantity of available data. In this way, the decision module 234 can generate appropriate recommendations in almost all scenarios.

  In one example, the algorithm used to generate the recommendation is controlled by a decision controller (also referred to herein as a “decision recommender”). For example, in one implementation, the decision controller can be configured to use five different algorithms to generate recommendations. In doing this, the decision controller operates to call the appropriate subroutines in the specified order and then sort the generated recommendations.

  According to an example, the decision controller can be configured to return recommendations within a certain time limit. For example, the decision controller can be requested to generate recommendations for a particular user or subscriber within 50 milliseconds. In one example, the decision controller can be configured to stop the recommendation generation process if the generation of recommendations takes longer than 50 milliseconds in this example. Recommendations generated within the allowed time are returned.

  By way of example, FIG. 13 illustrates an association (association rule algorithm), comparison (similarity algorithm), group classification (profiling algorithm), tracking (popular content algorithm), and networking (social networking algorithm) recommender according to one aspect. A block diagram of the relationship between algorithm modules 1104, 1106, 1108, 1110, and 1112 (collectively referred to herein as “recommender module 1114”) and decision controller 1116 is shown. As can be seen in the example shown in FIG. 13, each of the recommender modules association, comparison, group classification, tracking, and network recommender modules 1104, 1106, 1108, 1110, and 1112 is retrieved subscriber data. And processing these data to generate a recommendation list 1118. Each list is input to a decision controller 1116, which processes the results and outputs a final recommendation list.

  In one example, the recommender module 1114 can include data pre-processing 1122. The preprocessed data can be used when generating recommendations. This pre-processing stage can be configured to run at a specific time every day, run continuously, run offline once, and so on. In one example, this pre-processing stage cleans, processes, and structures the data into a format that can be used quickly and accurately during individual recommendation discovery times.

  In one example, the recommender module 1114 is configured to return a determined confidence level (1-5) 1124 indicating how much confidence each of the recommender modules 1114 has with respect to the recommended item. Can do. In one example, an item returned with a confidence level of “5” is considered to be a very good recommendation, and a recommendation with a confidence level of “1” is considered to be a bad recommendation. According to one implementation, each of the recommender modules 1114 can be configured to further have a confidence value that is an internal score. For each recommendation, the recommender module 1114 can generate a normalized decision confidence level using this internal score / confidence value. The decision controller 1116 uses the confidence level returned by each of the recommender modules 1114 and sorts each recommendation by using the weighting component 1126, the filtering component 1128, and the sorting component 1130. can do.

  By way of example, association, comparison, group classification, tracking, and network recommenders 1104, 1106, 1108, 1110, and 1112 may have the ability to provide functions A, B, and C, as shown below. it can.

  Referring to FIG. 14, the recommender processing method 1140 supports decisions by providing functions A, B, and C. Function A relates to finding items for subscribers determined to be applicable at block 1141. In one example, although described as receiving a request for recommendation, each of the recommender modules 1114 can be configured to have the ability to return a list of recommended items for the individual (block 1142). ). The function call is described as receiving recommendation parameters, but may employ several parameters including, but not limited to, minimum confidence level, number of recommendations, etc. (block 1144). The results can be ordered by confidence level (block 1146). In one example, recommended items are configured to be compatible with the personal mobile device (if information about the mobile device is provided) and do not include items purchased by the individual (block 1148). ).

  As shown in FIG. 15, an exemplary process flow for this individual recommendation function 1200 within the decision controller 1116 is that the portal API 1202 calls the decision access manager (DAM) 1204 using a getRecommendations call and the user or subscriber, Specifying the maximum number of recommendations, minimum confidence level, catalog zone, profile zone, mobile device, custom attribute containing filter, etc. can be included. The DAM 1204 can call the decision controller 1116 to pass user / subscriber identification, number of recommendations, minimum confidence level, catalog zone, profile zone, mobile device, custom attribute containing filter, etc. The decision controller 1116 calls each of the recommender modules 1114 using the subscriber information, the number of recommendations, and the minimum confidence level. Each of the recommender modules 1114 then adds these recommendations to the recommended item list if their recommendations are not already in the recommended item list and are defined above the minimum confidence level. Next, the decision controller 1116 sorts the list and filters the list by catalog zone, restricted content, device and custom attribute containing filters, and the like. In one example, only the requested “recommendation number” is returned to the DAM 1204. Subsequently, DAM 1204 can, for example, convert all content items in the list to xml and send it back to the caller.

  Returning to FIG. 14, the method 1140 performs a function B relating to finding a subscriber for a given item (the best subscriber to recommend a given item) determined to be applicable at block 1149. Further support. That is, each of the recommender modules 1114 is configured to have the capability of returning a list of users or subscribers whose items should be recommended (block 1150). In one example, the call requires several parameters, such as minimum confidence (block 1152). The results are ordered by confidence level (block 1154). The recommended subscribers are then filtered according to device suitability (block 1156).

  In one example, the process flow for this functionality shown in FIG. 15 within the decision controller 1116 is as follows. Specifying reliability, catalog zone, profile zone, etc. The decision controller 1116 calls each of the recommender modules 1114 using the number of users or subscribers, items, minimum confidence level, catalog zone, and profile zone. Each of the recommender modules 1114 other than the tracking recommender 1110 can add the subscriber to the list if the subscriber is not already in the recommended subscriber list. After calling all recommender modules 1114, the list is sorted and only the requested “number of subscribers” is returned to the GUI 1206.

  Returning to FIG. 14, the method 1140 further provides a post-purchase function C described as applicable at block 1157. That is, each of the recommender modules 1114 can be configured to include functionality to return recommended content or services for the subscriber based on previous items that the subscriber has purchased (block 1158). ). In one exemplary implementation, the post-purchase recommendation is a recommendation based on the item that has just been purchased, for example, a suggestion of accessories or consumables that are usually purchased together. The call may employ a number of parameters including item, minimum confidence level, number of recommendations, etc. (block 1160). The results can be ordered by confidence level (block 1162). The recommended items will be compatible with the personal mobile device (if provided) and will not have been purchased by the individual (block 1164).

  By way of example, the process flow for this function shown in FIG. 15 in decision controller 1116 is to call DAM 1204 using the getPostPurchaseRecommendations call with portal API 1202 and already purchased items, users or subscribers, maximum recommendations, minimum Specifying a confidence level, catalog zone, profile zone, mobile device, custom attribute containing filter, and the like. The DAM 1204 calls the decision controller 1116 with the purchased item, subscriber, number of recommendations, minimum confidence level, catalog zone, profile zone, mobile device, custom attribute containing filter, and the like. The decision controller 1116 calls each of the recommender modules 1114 with items already purchased, subscribers, number of recommendations, minimum confidence level, etc.

  Each of the recommender modules 1114 adds a recommendation to the recommended item list if the recommendation is not already in the recommendation list and exceeds the minimum confidence level. Next, the decision controller 1116 sorts the list and filters the list by catalog zone, restricted item, device, and custom attribute inclusion filter, and only the requested “recommendation count” is returned to the DAM 1204. Thereafter, DAM 1204 can, for example, convert all content items in the list to xml and send it back to the caller.

  By way of example, portal API 1202 is a mechanism that allows an external system to retrieve results from decision module 234. In one implementation, the portal API 1202 has three main purposes: (A) retrieve subscriber recommendations, (B) retrieve subscriber promotions, and (C) information about popular content. Take out. Similarly, the profile API can perform a fourth purpose (D) supplying certain information (eg, purchases) to the profile and recommendation system 101 in real time.

  In FIG. 15, the association recommender 1104 is also shown flowing to the item association accessor 1208. The comparison / similarity recommender 1106 flows to the comparison accessor 1208. Group classification recommender 1108 also flows to group purchase history accessor 1212 and group classification member accessor (not shown). Tracking recommender 1110 flows to tracking accessor 1214. Network recommender 1112 flows to subscriber network history accessor 1216.

  As shown in the example of FIG. 13, exemplary profile and recommendation system 101 includes association recommender 1104, comparison recommender 1106, group classification recommender 1108, tracking recommender 1110, and network recommender 1112. Can do.

  FIG. 16 illustrates the main parameters associated with the process flow of the recommender module 1114 comprising an association recommender 1104, a comparison / similarity recommender 1106, a group classification recommender 1108, a network recommender 1112 and a tracking recommender 1110. A more detailed block diagram of the decision module 234 including is shown. A summary of each of these recommenders is given below.

  Association (association rule) recommender 1104—By way of example, association recommender 1104 uses advanced association rule techniques to perform basket analysis on historical transaction data. Association rule data mining is a technique used to extract patterns in purchase history data. For example, in the supermarket shopping cart example, the association rule mining considers the combination of items in the customer's shopping cart, thereby making a simultaneous purchase relationship between all items for sale in the supermarket, for example, “Purchased bread” “Many people have also purchased milk”. The association rule can accurately grasp this relationship as one mathematical probability. Supermarket owners can study the total probability for all such paired items and make strategic arrangements by placing items on the shelf that are likely to be purchased at the same time. The idea in this case is to use historical transaction data to influence future purchase behavior.

  For example, if a database of transactions (eg, “shopping basket” in the supermarket) is provided for any collection of content, services, or merchandise (eg, downloads, ringtones, etc.), association rules between items Mining can be performed. Association rules allow intelligent recommendation of future content, services, or products to users, subscribers, or customers based on products that the user, subscriber, or customer has already purchased. Furthermore, in one example, the association rules can further find patterns that are more complex than simple relationships between pairs of items, such as “buying bread means that milk is likely to be purchased”. It is. More specifically, in one example, an association rule can link item sets together. An item set can be a collection of one or more items. For example, if A and B are two (non-coherent) item sets, ask “How likely is B to be in a transaction if A already exists”? Is possible. The latter sets an association rule represented by “A => B”. An association recommender 1104 incorporated within the decision module 234 may provide for the generation of multiple levels of association, eg, “AB => C, ABC => D”, maximum level 5 “ABCD => E”. (Ie, what is the probability that the same person will purchase item E if one person purchases items A, B, C, and D).

  In one example, the default configuration can be set to three items: the probability that a person will purchase item C if a person purchases item A, B. This setting can be deep enough for most facilities.

According to one implementation, two metrics can be defined to measure the strength of association rules, as shown in Table 1 below.

  In one example, by default, the associate recommender 1104 in the decision module 234 calculates the total transaction (shopping basket) number as the number of unique subscribers purchasing two or more items. This value is configurable.

  In one example, 100 is multiplied to represent the metric shown in Table 1 as a percentage. Support measures how often an item set occurs together in a transaction in the overall database, and confidence measures how often B appears in a transaction involving A. Association rules are quantified by strength in both these metrics.

  Determining all association rules that can occur between all possible item sets can be a computationally expensive task. In one example, to prevent the limitation, only so-called frequent item sets are noted, and the association algorithm can provide a highly optimized method for calculating association rules. In one implementation, the association algorithm may use a transaction from the transaction's subscriber history database table to generate an item association. In one example, only transactions with only the action type “Purchase” can be used to generate the association. In this example, only the “purchase” action type can be used when generating a recommendation.

In one example, action type, purchase, like, dislike, subscriber (SUB), non-subscriber (UNSUB), and non-purchase can be used. When using the latter action type in generating recommendations, the typical default weights shown in Table 2 can be used.

  For example, a “purchase” transaction can be considered more relevant than a favorite transaction. However, it should be noted that default weight values can be overridden.

  When generating a recommendation, the decision module 234 can retrieve the subscriber's past transactions 1304 retrieved via the profile API / interface data capture component 1305 and sum the weights per item. . This information can be used to search for any relevant combination of association rules. The results can be sorted with appropriate weighting.

  A typical flow of function calls in the association recommender 1104 to find an item for a subscriber is that the association recommender of the association module 1104 obtains the item that the subscriber has purchased and the content association module 1306. The association recommender looks for associations with the lowest support and confidence for each item set and specifies the number of recommended associations each item should return . The association recommender builds a query, obtains the item association from the item association store (eg, DB table, etc.) and confirms that the association support and confidence is below the minimum. Thereafter, several recommendations are generated, ordered by the overall confidence level.

  In one example, the association recommender can obtain results from the item association table for each item set. The association recommender can verify that these items are not already purchased by the subscriber by using the filtering process 1311, which takes into account device suitability and metadata filtering, access device information 1313. You can also put it in. The association recommender then adds these items to the list and applies the confidence level using the weighting process 1307 according to the weighting rules 1309. The recommendation is now returned to the decision recommender 234.

  In the typical flow of function calls within the associate recommender 1104 to obtain a subscriber for which an item is recommended, the associate recommender 1104's associate recommender (AssociateRecommender) is the item, minimum confidence level, catalog zone, profile zone, And the number of subscribers to be returned. The association recommender then queries the item association store (eg, DB table, etc.) using the target item, the number of subscribers to return, and the minimum confidence level, and purchases the item in the association that contains the target item. Search for all users or subscribers who have already purchased the item but have not yet purchased the item and verify that these users or subscribers have mobile devices capable of supporting the item.

  The typical flow of function calls within the associate recommender 1104 for post purchases includes the associated recommender of the associate recommender 1104 using a particular item to generate an item set that includes the target item. The association recommender then queries the item association store (eg, DB table, etc.) using the minimum confidence and support for each item set, and the recommendation to return (eg, using configurable properties). Specify a number. Next, the number of recommendations whose order is set by the overall confidence level is returned. The associate recommender confirms that these items have not already been purchased by the user or subscriber, adds these items to the list, applies weights, verifies the suitability of the device, and passes the results to the decision recommender 234. Send back.

  Comparison Recommender 1106 In one example, the purpose of the comparison recommender 1106 is to calculate the relationship between different content items based on the content metadata 1327 and receives input from a catalog API 1329 that captures interface data. By way of example, the comparative recommender 1106 can help solve a cold start problem with new items (ie, new items added to the mobile operator or its associated business system that have not yet been purchased by anyone). it can. In one example, the associated recommender 1104 may find a correlation for the new item since no subscriber has purchased the new item and the item does not exist in any subscriber purchase history. There are cases where it is impossible.

  In that situation, the comparison recommender 1106 can find items that are similar to a particular item and, for that item, find the subscribers for whom the item is recommended. In one example, the comparison recommender 1106 compares items using metadata such as artist, title, source name of included content, and publisher to form a set of content similarities 1310. It should also be noted by those skilled in the art that any custom attribute can be configured for use for comparison. In one example, the latter is achieved during the content custom attribute generation process.

  When comparing metadata, in one example, the comparison recommender 1106 can use a single metadata weight to determine how important these metadata are to compare attributes. The weight value can be, for example, 1 to 5, with 1 being the lowest value and 5 being the highest value. For example, a custom attribute can be referred to as a “price category” that can have a weight of 1, a genre weight can have a value of 5, and the similarity of two item genres is the similarity of prices. Is more important than

In one example, the compare recommender 1106 can have two mechanisms that can compare metadata values. The first mechanism can use string comparisons that are not affected by direct case. The second mechanism can use fuzzy string matching techniques. The second mechanism is appropriate when comparing values representing the same or similar values, eg, “FIFA 2004” and “FIFA 2005”. In one example, a mode is also provided that allows strings made up of substrings separated by commas to be compared.
In one exemplary aspect, a summary of the main operations in the function call process flow in the comparison algorithm 1106 for recommending items for subscribers is the similarity recommender of the comparison recommender 1106 purchased by the subscriber. Can then be used to query and set the item similarity store (eg, DB table, etc.) using the lowest confidence level for each purchased item. The number of similar items to be returned by each purchased item is specified using various properties. Thereafter, the similar items whose order is set by the similarity score are returned. In one example, the similarity recommender obtains a similar item for the item purchased by each user and verifies that the result has not already been purchased by the subscriber. The similarity recommender then adds these items to the list and applies a confidence level to each. The recommendation is now returned to the decision recommender 234.

  According to one exemplary aspect, an overview of the main operations in the function call process flow in the comparison algorithm 1106 for recommending a subscriber for recommending an item is that the item is passed to the similarity recommender of the comparison recommender 1106. And designating a maximum number of subscribers to reply specified by a configurable property.

  The similarity recommender then searches for similar items and finds subscribers who have never purchased the target item but have purchased one or more of the similar items. The similarity recommender then adds users to the list, sorts by confidence score, and applies a confidence level to each. The “number of subscribers” is now returned to the decision recommender 234.

  Group Classification Recommender 1108—In one example, the group classification recommender 1108 is implemented to calculate the most popular items purchased by a specified subscriber group (ie, subscriber group behavior 1324). In one aspect, this recommender 1108 is particularly useful for solving subscriber cold start problems when there is little or no historical information (purchased, likes, dislikes, etc.) about a particular subscriber. However, even in this situation, useful subscriber metadata or demographic information is available and recommendations can be made based on it. For example, if a particular subscriber is known to be a male postpaid business user, this information can be used to recommend content or services that other similar demographic subscribers prefer. .

  When generating recommendations for users or subscribers, the group classification recommender 1108 first determines the user group or subscriber group to which the user or subscriber belongs (ie, profile group member 1316) and relates to those groups. Take out the most popular content items. Group classification is provided by profile group generation process 1318, which supports subscriber attributes and transactions 1304 specifically for new members with no tracking records, and also supports profile group members 1316. The items are further filtered (eg, items already purchased by the user or subscriber are excluded) and sorted back by confidence level. In other examples, when recommending subscribers that are eligible for an item, the group recommender 1108 can determine in which subscriber group a particular content item is popular, and these groups 1316. Take out the member. The members of these groups are then filtered and returned by sorting by confidence level.

  By way of example, an overview of the main operations in the function call process flow in the group classification recommender 1108 for recommending items for a subscriber is as follows: And obtaining a group 1316 of which the subscriber is a member. The group classification recommender 1108 then stores the most purchased items for each group (optionally using profile zones and specifying the number of items to be returned (using configurable properties)). For example, it is taken out from a DB table or the like. Thereafter, the number of items whose order is set according to the purchase frequency is returned. The group classification recommender verifies that they have not been purchased by the user, adds these items to the list, and applies a confidence score. The recommendations are now sorted and returned to the decision recommender 234.

  In one exemplary aspect, an overview of the main operations in the function call process flow in the group recommender 1108 for a subscriber for whom an item is recommended is that the group recommender 1108 stores a data store (eg, a DB table) for that item. , Etc.) to obtain all groups in which the item has been purchased a configurable number of times. For each group, the group classification recommender obtains a user or subscriber who has not purchased this item and has a device that supports this item. The group classification recommender 1108 adds these subscribers to the subscriber list if the minimum confidence level is exceeded and checks whether the restricted content is acceptable for each subscriber.

  FIG. 17 shows a block diagram of the main components of network recommender 1112 according to one aspect. It includes a call data recording module 1402, a network builder module 1404, a network cleaning module 1406, a weighting module 1408, a subscriber relationship identification module 1410, and a network recommender module 1412. These modules are described in more detail below. In one example, the network recommender 1112 constructs a P2P (peer to peer) network 1322 using peer to peer (P2P) communication data 1320 (eg, calls, SMS data, etc.) captured by the network builder process 1321. These networks 1322 can be used to generate recommendations for users based on the behavior of members of the local network 1322. In one example, the network recommender 1112 uses a call data record (CDR) based on mobile device usage (conversation, SMS, MMS, 3G, etc.) and communicates with each mobile device pair of users. Build a network of links between them. This social network 1322 recommends items to people by polling their network links to study their purchasing behavior in his / her local network (or vice versa) Can be used).

  With continued reference to FIG. 17, according to one aspect, the network recommender 1112 comprises at least three modules, in particular, a network builder 1404, a network cleaner 1406, and a network recommender algorithm 1412.

  Referring to FIG. 18, a method 1420 for network recommendation according to one aspect is depicted. In one example, the network builder 1404 selects a CDR list as input from the call data recording module 1402, where the CDR is simply a data line that obtains communication between the two mobile devices (block 1422). A typical CDR can be “CALLER_NUMBER, RECEIVER_NUMBER, CALL_TYPE, CALL_TIME, CALL_DURATION, CALL_COST”. The utility can be configured to set one or more “filters” in the incoming CDR. In one aspect, the filter is a list of rules that the CDR must pass. In one example, the filter verifies the following: (A) Caller and recipient numbers have specified prefix and minimum / maximum length (block 1424); (B) Call specified type (voice, SMS, MMS, video, etc.) (C) a voice call is within a specified minimum / maximum duration (block 1428), (D) a CDR is present at a specified start / end date in the calendar, and (E) The CDR time exists between the specified times (block 1432). CDR days of the week are also filtered (block 1433).

  Each set of filters can also define a separate network. For example, one such network can obtain CDRs representing communications from Monday to Friday from 8 am to 6 pm—ie, a business network. Other networks that obtain CDRs from Friday 6pm to Monday 2am are social networks.

  In one example, any data record passes through the filter and is sent to the “Network Summary” table, and in one example, the P2P link is stored and aggregated as a single row (block 1434). That is, when the CDR from the caller A to the receiver B arrives, the presence / absence of existing rows related to A to B is confirmed in the table. If a line already exists, it is updated with the information in the new CDR. Otherwise, the CDR is inserted as a new line. In this way, each line in the final network summary table represents the total communication activity between the two people.

  According to one example, it is configured to be directed to each of these networks (block 1436), and the link “A ---> B” is considered different from the link “B ----> A”. Thus, if CDRs exist for A to B and B to A communications, each is placed in a different row in the network summary table. In one example, the filter can be set and configured in an XML file and can be called, for example, “RawRecordFilters.xml”.

  In one example, the network summary table includes a certain amount of noise, such as automated services such as train timetables, weather, news alerts, taxi drivers, and the like. The data is not useful from a recommendation point of view, so the network recommender 1112 is designed to delete the data from the network summary table using the cleaning module 1406 (block 1438). According to one aspect, for each network built in the network builder module 1404, it is possible to build a separate filter that operates to clean only that network. The filter may specify, for example, the maximum / minimum number of outgoing calls that the caller is allowed to have (block 1440). Accordingly, callers that violate these thresholds are removed from the network summary.

  In one example, when the network summary table is cleaned, the network recommender 1112 is configured to assign a weight (or strength) to each relationship (ie, a row in the table) by the weighting module 1408 (block 1442). ). This is to allow the person's link to be ranked to identify his / or her “best” friend (ie, the friend with the strongest weight) (block 1444). In one example, these filters can be set up and configured in an XML file, eg, “Network SummaryTableCleaner.xml.”

  Once the network summary table has been constructed and cleaned, the network recommender 1112 can be used to find items that should be recommended to subscribers or to find subscribers for whom items can be strongly recommended. Yes (block 1446). In one example, for a given subscriber, the subscriber's “neighbors” (people directly linked to him / her) and their neighbors, and so on, are identified by the subscriber relationship identification module 1410 (here (Referred to as “degree of separation” or simply degrees) (block 1448). Thus, a person who is two times away from an individual refers to an individual (ie, a friend of a friend) linked to someone that the individual is linked to. In one example, the most popular items among these people form recommendations for subscribers.

  The network recommender 1112 can be configured to consider people up to 5 degrees away from the subscriber. The network recommender 1112 may be further configured to include only people within a degree of separation whose pairwise weighting exceeds a certain intensity (block 1450). In this case, the higher the weighting threshold, the closer the relationship becomes.

  According to an example, network recommender 1112 can be used to recommend items to subscribers with a purchase history. In the scenario, the subscriber's purchase history can be used to generate recommendations for the subscriber and to exclude items that have already been purchased (block 1452).

  In one exemplary aspect, an overview of the main operations in the function call process flow within the network recommender 1112 includes the decision controller 1116 obtaining items that the subscriber has purchased, and including them in the network It is passed to a network recommender 1412 of the recommender 1112. In one example, network recommender 1412 uses the passed threshold to determine how often to search in the subscriber's local network.

  In one example, the network recommender 1412 searches the local network for items purchased by members of the subscriber's local network that are within a specified degree of isolation, and has a P2P with a weight greater than a specified threshold. Follow links only and poll only the members who make the specified and configurable minimum number of purchases. The network recommender 1412 then verifies that each item has not already been purchased by the target subscriber, adds these items to the list, and applies a confidence level to each. The recommendation is now returned to the decision module 1116.

  In another exemplary aspect, an overview of the main operations in the function call process flow within the network recommender 1112 to find a subscriber for whom an item is recommended is that using a given item and And finding people who have purchased. Next, each of these people's neighbors become targets for the item. Notably, it can be specified that only neighbors that have a higher weight than a certain threshold and are within some degree of separation can be considered. Within each local network, people who have not purchased a given item can be identified, and only those people whose purchase count exceeds the lowest configurable level are added as recommendation targets. The

  Returning to FIG. 16, the tracking recommender 1110 is described. In one example, tracking recommender 1110 can be used to monitor and record the most commonly used content or services, ie, to provide overall trend tracking 1330. This information is used to send back recommendations based on the most popular items. In one example, tracking recommender 1110 can be used to target popular items to subscribers. In one example, the results from tracking recommender 1110 can be used when other algorithms are unable to provide a more specific recommendation for the target (ie, during a subscriber cold start situation). Tracking recommender 1110 can further be used to search for the most popular content in the catalog during a specified period of time. An example of this type of recommendation is to display a “hot” list to portal users. In one example, tracking recommender 1110 uses a tracking content item table constructed from transaction data in a subscriber history table. In one example, the time to review historical transactions can be specified before generating the tracking content item table.

  In one exemplary aspect, an overview of the main operations in the function call process flow within the tracking recommender 1110 for recommending items for a subscriber is supported by the track recommender (TrackRecommender). And obtaining the most popular items that have not been previously purchased by subscribers. The tracking recommender then uses the profile zone to specify the number of items to be returned using configurable properties. The track recommender builds a query, gets the items from the tracked content item table, and returns a specified number of items ordered by the count of each item. The tracking recommender then checks the restricted content, obtains content that has not been purchased by the subscriber or user, adds the item to the list, and applies the confidence level. The recommendation is now returned to the decision controller 1116.

  In a further exemplary aspect, the profile and recommendation system 101 of this disclosure can use a best-seller recommender.

  However, according to one aspect, the best selling recommender is separate from the recommender 1114 and may rather be referred to as a stand-alone module. In one example, the latter is reflected in the code level that defines best selling recommenders outside the decision recommender class architecture. In one aspect, recommender 1114 is aimed at finding items for subscribers, or items for other items, while best-seller recommenders can be further considered to be statistical tools.

  The best-seller recommender, in one simple example, allows the user to retrieve the most popular content from the subscriber history transaction table, and the user behavior (purchase / like / dislike / view, etc.) and duration ( Search based on past time, eg, last 1 hour, last 12 hours, last day, last week, etc. In one optional aspect, passing the subscriber into the best seller recommender has two additional effects with respect to the item to be returned. That is, (A) items that have been purchased by the subscriber are hidden, or (B) if applicable, restricted items are hidden from the subscriber.

  In one example, when a subscriber is passed, functionally, the best selling recommender can operate very much like the tracking recommender 1110. In one example, the best selling recommender can consider actions other than purchases. Further, similar to tracking recommender 1110, best selling recommender data can be stored in the tracking content item table along with the tracking recommender data and share the same format. In one example, setting up bestseller data generation may consist of adding a single property with a comma-separated list of acceptable periods. For example, the property “1H, 7H, 1D, 7D, 999D” indicates five different periods (1 hour, 7 hours, 1 day, 7 days, 999 days), and the best seller recommender searches back in time. It is possible. The preparation phase can divide the subscriber history transaction data table into the times specified in the above properties and store this information in the tracking content item table along with the tracking recommender data. If the time specified by tracking is one of the times specified by the best seller recommender, data generation for that period can occur only once.

  In one example, the best selling recommender is available only as an API call GetTopContentByTimeAndAction. In one aspect, simple object access protocol (SOAP) parameters, such as behavior (purchase / view / like, etc.), or a comma-separated list of actions, and duration (eg, 12 hours or 7 days, etc.) Can be essential.

  It will be appreciated that the parameters provided in the decision call provide a recommendation generation mechanism. They combine the business rules with the inputs to the decision recommender / outputs from the decision recommender to produce the final recommendation given to the subscriber. These parameters further limit the output from the decision recommender.

  In one example, specifying parameters is accomplished via provided API call parameters. Furthermore, global parameter default values for the entire system can also be specified. According to one aspect, the decision module API parameters can be used to provide the following capabilities: That is, (A) Recommender selection, (B) Rules used to select exactly what recommender configuration decisions will be used to make recommendations, (C) input criteria, (D) subscribers And content filtering criteria are passed as parameters to the decision module, which are used when reviewing available profile and catalog data, (E) query results, and (F) output from the decision module. It is determined whether good recommendations have been found. The decision module 234 returns one level of certainty value for each recommendation made by the decision module 234 as a confidence level. The rules may specify the lowest acceptable level of certainty that other or different recommenders will be attempted if not achieved.

  For example, according to one aspect, a decision call is made to request a recommendation return to the subscriber from the available content in the catalog module. The decision module can return with recommendations, but with a low level of certainty. In this situation, the recommendation can be ignored and the specific (constant) content item that the administrator 213 is eager to promote can be defaulted.

  Another example is an example where a content item for cross selling is required. The decision module 234 can recommend content or services that may not be the clearest or most common option. The latter is used to avoid making recommendations that have little value to the subscriber (eg, recommending the last hit of a particular artist when the subscriber has just purchased the most recent release) be able to.

  By way of example, subscriber filtering can be used to allow the administrator 213 to specify which subscribers can make a particular type of recommendation. This is used when generating recommendations for a particular subscriber (eg, online recommendations shown on a portal). For example, a decision rule can be generated that restricts the presentation of recommendations to subscribers with a particular type of device or attribute (eg, postpaid high-payers).

  In another example, subscriber filtering can be used to preselect a subset of subscribers for which recommendations are to be generated. This is used when generating outbound promotions based on this rule. For example, the administrator 213 can specify subscriber filtering criteria via the user interface of the recommendation application 212, which allows selecting attributes from a list and performing simple OR logic. (E.g., MMS devices and prepaid, postpaid, youth, etc.).

  With respect to content filtering, content filtering can carefully select the type of content that is recommended. For example, the administrator 213 can generate rules that only recommend MMS content items. Thus, in one example, the API associated with retrieving recommendations also has the ability to specify certain criteria (eg, genre of music track, etc.) regarding the type of recommendation to be returned. While this allows for greater control by the calling application, changing the user experience may require re-encoding of the application making the API call. To avoid the latter, the profile and recommendation system 101 can provide a recommendation profile. The recommendation profile can be generated via the user interface of the recommendation application block 212 (shown in FIG. 4), allowing the administrator 213 to specify values for all parameters of the recommendation API call. . The calling application refers to this recommendation profile instead of specifying values for individual parameters. The decision module 234 adds parameter values from the profile. The recommendation profile now allows the administrator 213 to change the user experience without requiring code redevelopment.

  According to an example, the response time that the decision module 234 can provide services may be important. Response time criteria can be measured in units of 10 seconds or 100 seconds or milliseconds and meet hundreds of requests per second. In one example, factors that can affect performance may be the number of recommenders used and the amount of data of interest.

  According to one aspect, FIG. 19 is a schematic diagram illustrating how the decision module 234 uses a web container 1502 to meet these performance requirements. In one example, several different techniques can be used to meet performance requirements, such as caching in the cache subsystem 1504 and database access to the database 1508. The decision module 234 can use intelligent caching of frequently accessed data to improve performance, according to one aspect. The amount and lifetime of the cached data elements can be controlled to suit the amount of data available and available hardware resources. According to another aspect, if caching is not deployed, the decision module 234 can use fine-tuned SQL and JDBC for example to retrieve data. All SQL and database schema objects are designed to provide maximum performance and scalability.

  The caching mechanism in decision module 234 can utilize several different caches 1512 to store different types of frequently accessed data. The most commonly used caches are information about subscriber history, item data including custom attributes, and data generated by different algorithms during stage I (eg, item association data, similarity comparison, network, etc.) The cache to keep.

  Data access layer 1518 is used to abstract data loading from other components of decision module 234. When a request for data is made, the data access layer 1518 first checks the caching subsystem to see if the data is already loaded. If the data is already loaded, the data is returned. If the data is not loaded, the data access layer 1518 loads the data from the database and inserts the data into the cache before returning it. The caching subsystem 1504 manages the purging of old data or unused data. In one example, the caching subsystem 1504 uses both the last access time of one data and the overall cache size to determine what should be parsed.

  In one example, one consideration within decision module 234 is the amount of time required to perform the stage I data preparation described above with respect to FIG. Note that while this process does not have the same impact on user experience, irrational hardware resources are not required, and that this process can be completed in a reasonable amount of time. It is. In one example, if possible, the stage I analysis described at 1522 is performed in an incremental manner (ie, processing new data and combining the new data with existing results).

  Referring to FIG. 20, the main components of the sales promotion module 236 are described by way of example. As described with respect to FIG. 4, in one example, the promotion module 236 generates related outbound promotions and increases the dynamic content page on the web portal to increase customer ingestion and commitment. Provides the ability to support creation. The sales promotion module 236 includes a sales promotion management module 1602, a sales promotion feedback module 1604, a sales promotion product generation module 1606, a sales promotion product retrieval module 1608, and a sales promotion product distribution module 1610. In the following, the function of each of these modules will be described in more detail.

  With reference to FIGS. 4 and 20, in one example, in a promotion, an administrator 213 generates several campaigns for a particular item. For example, promotions can be generated for certain items that a company is trying to introduce to people. This can be done by the administrator 213 via the recommendation application 212 in communication with the promotion creation module 1606 and the promotion management module 1602 or as described below (FIG. 4). ).

  In one example, the profile and recommendation system 101 can determine the most relevant promotions for individual subscribers and the most appropriate time to deliver these promotions. By making these promotions interactive, mobile operators can maximize the opportunities for subscribers to make purchases. For example, if a mobile operator wants to build a swords section of a mobile operator portal, the profile and recommendation system 101 can use all the content and services in a single page or section of the portal without manual intervention. (For example, ringtones, screen savers, desktops, trailers, movie reviews, games, ticket services, etc.) can be grouped. It should be noted that in the example, only content and services applicable to subscribers or users and their mobile device capabilities are displayed.

  Further, according to one example, the online behavior of users or subscribers is profiled, and a list of these users or subscribers' purchasing habits, and successful promotions and offers made is generated. All this information can be used to define the best way to promote new offers and the best time / day of the week. In this way, promotions are delivered to users or subscribers when they have the highest statistical chance of success and through the channel that the subscriber is most likely to respond to. For example, if a subscriber sends five or more peer-to-peer messages from 5:30 pm to 6:30 pm on weekdays, this pattern can be identified and used as a promotional window for that subscriber. it can. Most likely, the subscriber appears to be commuting by train or bus, and is also using that time for exchanging information with friends and setting up evening entertainment, and promoting sales to these subscribers. The best time to do it.

  Promotion module 236 provides targeted automated promotions across multiple channels by providing intelligent automated means to drive the use of content services through both online and outbound mechanisms. It can also be possible to generate. Sales promotion items are distributed via the sales promotion item distribution module 1610. In one example, the on-line mechanism is a promotion utilized for subscribers visiting a mobile operator's wired or wireless portal. Outbound promotions are promotions that are sent to users or subscribers via mechanisms such as SMS, MMS, WAP push, etc.

  In one example, online promotions are facilitated by communication between the promotional item retrieval module 1608 and the service and content information component 208, which in one example comprises a portal 226. . In one example, this integration may include portal applications programming interface, portal retrieving information from promotion retrieval module 1608 (eg, best promotion for a particular subscriber, etc.) and promotion feedback module 1604. Via a SOAP based API that allows usage information (e.g., the subscriber is interested in a promotional item, etc.) to be sent back via. Promotion module 236 works with the portal to track which subscribers have visited the portal, which promotions are being viewed, and which are associated with click-throughs. Can do. The use case is an example when the portal requests the best advertising details for a particular subscriber via the promotion retrieval module 1608. The promotion module 236 returns details of the advertisement (including text, images, links, etc.). The portal presents this advertisement at a corresponding position on the site.

  In addition to the portal API providing information to the portal, the portal can also feed information back to the promotion module 236 through the profile feedback module 1604. This allows the promotion module 236 to learn more about subscriber behavior and to function more effectively. Examples of this include reporting that a subscriber has visited the portal, and a sales promotion that resulted in a purchase and a sales promotion that did not result in a purchase. Since the promotion module 236 is aware of this information, the promotion module 236 can provide a report on the effectiveness of the campaign.

  According to one aspect, different types of online promotions can be performed by the promotion module 236, each of which is a different way to present a targeted promotion to the subscriber when navigating the portal. I will provide a. The following are typical online promotions.

    Banner advertisements: These are targeted advertisements placed on a portal that can be viewed and selected by a user or subscriber. When selected by the subscriber, the promotional details are shown to the subscriber and the subscriber can proceed to purchase if desired. In one example, the banner advertisement is a graphic advertisement. Banner ads can also be defined using a display range that allows promotional items to be displayed in the relevant places on the portal (for example, sales of ringtones in the ringtone section as opposed to the financial news section). Indicates promotion).

  Post-purchase advertisements: These are advertisements that are shown to subscribers following a purchase. The cross-selling content can be configured by the profile and recommendation system administrator 213 or can be automatically generated by the decision module 234.

  Bundle: Individual content or services can be grouped together and offered to subscribers at a discounted price. The bundled information is sorted in the promotion module 236 and can be retrieved by the portal. When a subscriber purchases a bundle, the portal manages fulfillment in collaboration with the profile and recommendation system. In one example, the promotion module 236 records bundle purchases for future use.

  Target specific menu links: These are links placed within the portal menu structure, where targets are specified to direct the subscriber to a portal area where the relevant content or service can be offered to the subscriber. Placing these links in association with other static or personalized links is controlled by the portal management system.

Outbound promotions are based on outbound broadcasts and several inbound communication mechanisms such as: That is,
Broadcast: Promotional broadcasts for subscriber groups can be generated by the promotion management module 1602. Broadcast messages can be SMS, MMS, or WAP push messages and can be useful in promoting new content and services.

  SMS based campaign: In the case of SMS, in one example, the promotion module 236 automatically manages subscriber content / session information. The administrator 213 specifies, through communication with the promotion management module 1602, the message text that the subscriber should give to proceed through the conversation (regular expressions are supported to provide a stronger match). A default generic sentence can be added to get inaccurate messages and to return context sensitive help messages.

  Campaign based on WAP: When using the WAP channel, the administrator 213 can specify individual pages to be displayed on the mobile device. These pages can include text, images, data entry fields (input data is stored as variables), links to external WAPs, and so on.

  In one example, the broadcast can be delivered as a WAP push message by the promotion delivery module 1610 and directs these subscribers to an online version of the promotion when activated by the subscriber. This is provided in three ways: That is, (A) the link included in the WAP push message points to another system (eg, portal, etc.) where information about the promotional item is available. This can be an ideal mechanism for letting subscribers recognize new services on the portal, (B) details of responding subscribers are recorded and other systems (eg, portals, etc.) Redirected to. This can be useful when real-time tracking of the response by the subscriber is required. Promotion module 236 provides a real-time online report showing the total number of respondents, and (C) the link points to a page containing the details of the promotion. In one example, the administrator 213 generates the promotion page via the What You See IS What You Get (WYSIWYG) editor that communicates with the promotion generation module 1606. can do. Promotion module 236 can provide promotion information including images to mobile device hypertext markup language (HTML), wireless markup language (WML), and extensible hypertext markup language (XHTML). Subscribers can view promotion information directly from the promotion module 236 and can include images and links to external portals. Use this solution when you can use a single WAP deck to communicate promotions (eg, flyers, etc.) and to provide more detailed information or a progress link to a purchase dialogue Can do. This option allows for quick promotion creation without the need to update the mobile operator portal.

  In one example, part of the outbound promotion is to provide an inbound communication mechanism for the subscriber to follow up the promotion. Promotion module 236 provides the following mechanisms.

  Interest groups: Subscribers can register their interest in a particular promotion or topic by responding with simple keywords (eg, via SMS). Promotion module 236 can automatically track which items the subscriber is interested in and store this information for future use. This can provide an ideal way to build a subscriber-based marketing mechanism, such as a loyalty group or community.

  Interactive Campaign: The promotion module 236 operates to support the creation of more complex campaigns and interactive services by the promotion creation module 1606 through the use of a conversation scripting application (CSA). The CSA generates these conversation scripts by allowing the administrator to drag and drop components or called statements on the CSA screen, and the various that a subscriber can follow when participating in a campaign. Provides a graphical way to link these together to create a simple conversation path.

  In one example, an integrated simulator can be provided that allows campaigns to be easily tested and verified. In addition to providing a powerful campaign service generation environment, the conversation engine can provide very high performance that is scalable to interact with hundreds of subscribers per second.

  In one example, outbound promotion execution may be performed, for example, by an external CRM or marketing automation system, such as Infor Global Solutions GmbH in Alpharetta, Georgia (Info Global Solutions GmbH), Unica Corporation in Waltham, Massachusetts, Alternatively, it can be implemented via the subscriber profile information source 210 when it is equipped with Chordant Software, Inc. (Chodianto Software Inc.) Epiphany of Cupertino, California. This integration can be as simple as creating a broadcast, or can be extended to the creation of interactive campaigns with full features whose results are fed back into the CRM or marketing system. It is. In one example, this integration is provided via a promotion group and a broadcast management API.

  Promotion module 236 also allows targeted targeted promotions to be achieved through the creation of profile groups. These lists can be imported by the profile and recommendation system or generated using accumulated subscriber usage information.

  The promotion module 236 can be deployed alone or in conjunction with other modules of the profile and recommender system, including the profile module 232, the catalog module 230, and the decision module 234. When deployed alone, the promotion module 236 provides the administrator with the ability to quickly generate and execute a targeted promotion for the subscriber specified by the promotion generation module 1606. The promotion module 236, when used in conjunction with the profile module 232, catalog module 230, and decision module 234, is an automated, sophisticated, more specific target that will achieve a higher success rate. Have the ability to provide promotional materials. This is possible because promotion module 236, when used in combination with other profile and recommendation modules, can utilize decision capabilities to provide a better promotion selection for online subscribers. This is possible. In the case of decision module 234, rules and algorithms are used that utilize the information in profile module 232 and catalog module 230 to reach the best possible promotion for the target subscriber. Proposed outbound sales promotions are also automatically calculated and presented to the user. The determination module 234 can select the generated promotions and determine which subscribers to target in the outbound manner. Vendor creation can also be provided from information stored in the catalog module 230. In one example where no other profile and recommendation system components exist, the administrator 213 needs to manually identify the content / service that he / she wishes to promote. However, when the decision module 234 is used, the decision module 234 automatically recommends recommendations (eg, after purchase, outbound broadcast, etc.) directly from the information stored in the catalog module and profile module, 232 and 234, respectively. Can be generated.

  Promotions can often include discounted prices for targeted content or services. The promotion module 236 allows the administrator to predetermine the content fee by providing this discounted rate information for use during the promotion. Depending on how billing integration is achieved, this promotion fee information can be passed directly from the promotion module 236 to the billing system or passed to the portal and from there to the billing system. Promotion module 236 also provides the administrator with web-based administrative features, and can provide the ability to effectively manage numerous simultaneous broadcasts by interfacing with promotion management module 1602 and It can be easily scaled up to deliver millions of messages per day. These features can include: (A) Customizable entitlement process based on specific details about the broadcast such as quantity, throttling, time, etc., (B) broadcast time limit (e.g. 9 am from Monday to Friday) (C) Generated message throttling according to network guidelines, (D) Currently running or future broadcast every day that can be disabled by the administrator if necessary And weekly browsing, (E) limit on the number of messages that can be sent per day-this can be a requirement set by the network, (F) real-time reporting and management for long-term broadcasts. The report indicates the percentage of broadcasts completed and the estimated end time. The administrator can easily pause, resume, and stop the broadcast, and (G) repeat the broadcast daily, weekly, and monthly.

  The automation provided by the promotion module 236 is very sophisticated and allows a number of smaller (targeted more) promotions as opposed to a smaller number of large general promotions. It is possible to execute. In addition, the promotion module 236 allows the broadcast definition to be tailored to a particular subscriber. For example, it is possible to manage the number of broadcasts that a subscriber should receive within a certain period, the time at which the subscriber should receive the broadcast, and the preferred contact mechanism of the subscriber (eg, SMS, MMS, etc.) It is.

  In one example, the promotion module 236 can generate and distribute unique coupons as part of a broadcast or follow-up channel. These can be used to redeem certain benefits such as discounts, credits, gifts, merchandise, etc.

  According to one aspect, variables can also be used in broadcast text to optimize content for subscribers. These variables are subscriber-specific values that can be set by the administrator 213, the external system, or the subscriber himself. For example, the subscriber's name or balance can be included in the broadcast. Variables can also be used to implement loyalty points, and subscribers will accumulate points each time they respond to a campaign. Variable values can be accessed by external systems via XML API.

  In one example, the promotion module 236 may provide a real-time report regarding the success (or failure) of the promotion. It can provide statistics on the number of subscribers who have viewed, shown interest, responded to, or rejected a particular promotion. Being able to see these results in real time means that regularly executed events can be immediately modified to ensure future validity, successful events can be re-executed, and unsuccessful events can be eliminated. To do.

  According to one aspect, promotions can be accessed through the portal API in a manner similar to recommendations. Portal APIs provide several APIs for the purpose of requesting promotions and feeding back user events associated with promotions (eg, click-throughs) into the profile and recommendation system. In another example, the promotion may include the user interface of the recommendation application block 212 and the promotion management module 1602 for the purpose of generating an outbound promotion via SMS, MMS, WAP push, etc. Can be accessed via communication between. In this case, the promotion distribution module 1610 can be used to actually distribute outbound promotions or simply to generate a list of target subscriber phone numbers. When generating the target subscriber list, it is possible to specify both the number of subscribers and the required minimum confidence level. This means that the top 100,000 subscribers to be identified as targets for a particular item can be generated with a certain minimum confidence level.

  According to one implementation, the creation of a promotion by the promotion creation module 1606 includes three stages: a first stage (general details), a second stage (target subscriber), and a third stage (distribution). Can do.

  In the first phase, the administrator 213 specifies some general details about the promotion, (A) name and description, (B) type of promotion (eg, banner ad, WAP link, bundle) , Cross-selling, etc.) (C) Weight-Promotional Weight is used when selecting the best promotional for a subscriber when there are two or more candidate promotions . For example, if there are two promotions that specify the same profile group, the weight is used to determine the exact promotion for the subscribers in this group. (D) Offer item- Specific content from the catalog module 230 (eg, Pack Mac Java game, etc.), or a category of content selected from the catalog module 230 (eg, chord lock ringtone), (E) Link to content item hosted by external system (eg, business headline), (F) Coverage-Promotion scope identifies the area to which this promotion applies, for example, portal ringtone To generate a promotional product with a ringtone scope that specifies what should be indicated in Kill.

During the second phase, the administrator 213 is requested to specify a mechanism that identifies the target subscribers that are the subject of this promotion. The target group specifies the subscribers for whom the promotional material can be presented. There are three example choices. That is,
Option A. Global—A promotion whose target is globally specified can be shown to any subscriber as long as the subscriber has not viewed the promotion a certain number of times or purchased a promotional item. Global promotions can have a lower weight than promotions where other targets are more specific.

    Option B. Profile group-administrator 213 can also specify one or more profile groups from which promotions are available. These groups can be a list of subscriber phone numbers imported into the profile module 232, or can be generated by the profile module 232 from recorded subscriber activities or attributes.

    Option C. Decision. When the decision module 234 is utilized to determine whether a particular promotion is to be shown to the subscriber, the decision module 234 first determines an expanded list of recommendations for the individual and the sale Compare this list with the items contained in the promotion. If any item in the promotion is on the user's recommendation list and the subscriber has not purchased any item in the promotion, the promotion is shown to the subscriber. Qualified. If this targeting mechanism is selected, it is possible to select the minimum confidence level that the promotion should have in order to be eligible to display to the subscriber.

  In the third stage, the administrator 213 specifies a promotion catalog that is used to present promotional items to subscribers. This can be divided into an online promotion catalog and an outbound promotion catalog. In the online promotion catalog, the administrator 213 is prompted to provide text or graphics based on the web and WAP displayed in the portal. In one example, both a general promotional catalog and a detailed promotional catalog are specified. Initially, summary information is presented to the subscriber and detailed information is presented when the subscriber requests more information about the promotional material. This type of promotional catalog is made available to the portal via the portal API. In the outbound promotional catalog, the administrator 213 is configured to supply promotional catalogs for different outbound mechanisms that the user wishes to use. The administrator 213 can specify content such as SMS, MMS, WAP push, and the like. This information is used by the promotion distribution module 1610 when executing outbound promotions.

  FIG. 21 shows a flowchart of an exemplary process flow 1700 within the promotion module 236. At 1702, a list of promotions generated by the administrator is retrieved. At 1704, a recommendation is generated for the subscriber. At 1706, the promotion is compared to the recommendations for the subscriber, for example by checking for a match. At 1708, promotions that are in the recommendation list but have not been purchased by the subscriber are determined (in one example, previous processing may make this confirmation redundant). At 1710, the promotion list is passed to an external application. External applications can send these promotions to these subscribers for delivery to subscribers via SMS, MMS, WAP push, etc. messages online, via a web portal, or outbound. Can be passed to the device.

  In addition to the above-described modules of the profile and recommendation system, according to an example, in the profile and recommendation network 1800, the profile and recommendation module 1801 can include additional modules. FIG. 22 shows, in accordance with one aspect, details of the above components of FIG. 4 of the profile and recommendation system, along with content module 1804 and connection module 1802.

  The content module 1804 provides content management and distribution capabilities for a range of content and services. Connection module 1802 enables distribution of SMS, MMS, WAP, and downloadable content. By way of example, all industry standard network connectivity protocols and distribution protocols are supported. The content module 1804 may operate to integrate with a subscriber profile information source 210, for example, billing, for billing for content or services. Further, the content module 1804 can be integrated with prepaid and postpaid systems via various protocols. A content module 1804 integrates with the service and content information block 208 to indicate content or services (eg, titles, artists, previews, etc.) available on the web or WAP portal and to trigger delivery of the content or services. It is also possible to do.

  In one example, the content module 1804 provides the ability to store, manage, and distribute any content type locally. Content and information can be stored and managed in a secure manner, for example via a web interface, and distributed via carrier grade downloads, alerts, and on-demand content servers.

The profile and recommendation system can further support various mechanisms for automatic reception and collection of content from external sources. The platform can be configured to receive content feeds in the form of HTTP / XML or File Transfer Protocol (FTP) / XML from an external source, providing a framework for implementing content integration mechanisms dedicated to content providers . According to one aspect, the profile and recommendation system can also retrieve content in advance from an external source such as RSS. In one example, the profile and recommendation system content submission API can be used by content providers to manage content using the XML format defined in HTTP.
The content module 1804 can be further configured to provide active or inactive updates depending on the content type validation that may be required. The administrator 213 can provide the type of authorization required for each type of content. In one example, trusted content can be automatically validated, and other types of content can require approval from an administrator 213 or a mobile operator's content manager.

  Further, the content module 1804 can support subscription-based alert generation and management and delivery of SMS, MMS, or other content types. Subscribers can generate personalized alert schedules dedicated to their interests and define parameters such as bearer (eg, SMS vs. MMS, etc.), delivery time, language, time zone, etc. Is possible. The alert module of the content module 1804 can scale to meet the needs of the mobile operator and deliver content or services in a timely manner.

  By way of example, the content download module provides a download server for any downloadable type of content including, without limitation, Java, ringtones, wallpapers, etc. In one example, the content download module provides the following features. That is, (A) Java application (for example, game, etc.), Java archive (JAR) or Java application development (JAD) format (two-stage download) distribution, (B) A unique URL is assigned to each download. Can and can have its own token ID, (C) the JAD file is rewritten to specify the dynamic location of the JAR download, (D) download retry only for a configurable period or configurable attempt (E) Digital Rights Management (DRM) can be applied to downloaded content, (F) Downloads can be initiated via a WAP push or directly from the WAP portal And (G) CSR activities related to user activity search Interface is based on the mobile subscriber integrated services digital network number (MSISDN), if required has the ability to re-send the download.

  The module can be configured to use virtually all possible criteria and techniques to ensure successful downloads and accurate billing of downloaded content. This can include a download notification API that allows the download server to notify the external system when different stages of the download occur. These notifications can be used to stop the download at any point in time or to generate a billing event.

  According to an example, the connection module 1802 can be configured to have digital rights management (DRM) capabilities and is open mobile alliance (OMA) DRMv1 forward for selective content defined by a platform administrator or content provider. Provides the ability to apply lock, combined delivery and separate delivery.

  In one aspect, the connection module 1802 includes a transcoding engine that can be configured to support transcoding between a wide variety of content formats and codecs. Furthermore, the transcoding engine can be configured to provide its own device profile database that has been tested and tailored specifically for delivering multimedia content.

  According to one aspect, the connection module 1802 can process three content distribution scenarios as follows.

  Scenario 1. Information on demand: In this scenario, service and content requests map those services or content requests to the appropriate content source, retrieve the current content or service from that source, and retrieve it to the subscriber. It is processed by replying to.

    Scenario 2. Scheduled delivery: Scheduled delivery can be based on a fixed delivery schedule specified by the system administrator 213 or on a schedule defined by the subscriber. In this situation, the content or service is retrieved and delivered to the subscriber at the time specified in the schedule.

  Scenario 3. Unscheduled delivery: Delivery of unscheduled content or services can be triggered manually or automatically via an external event. In this situation, the content or service is recommended to the subscriber from the content source or service source.

  The content module 1804 can be integrated with an existing portal via the provided portal API, or in situations where an existing storefront is being exchanged, the content module 1804 can be tailored to the mobile operator's requirements. And provide a storefront that can be customized. The content module further provides an “out-of-the-box” storefront, allowing mobile operators to commercialize content or services across multiple storefronts and multiple delivery channels. To do. This default storefront can be customized to meet specific mobile operator features and brand requirements.

  In one example, the storefront is pre-integrated with the rest of the profile and recommendation system so that the storefront can best utilize the overall system features. According to one aspect, the storefront allows the mobile operator to (A) offer a comprehensive range of services to subscribers, (B) promote new services, and (C) generate offers for content bundles. (D) providing a “user friendly” interface for subscribers to purchase and subscribe to content services; (E) displaying a storefront market segment specific version; and (F) new / It may be possible to create a top 10 list for promoting popular services.

  In addition, the storefront allows the subscriber to (A) browse the full range of content services offered (all services or services available in the market segment), (B) content services (eg games, ringtones). , Etc.), (C) applying for content services (eg, alerts, etc.), (D) managing application for content services, and (E) specifying their own content distribution schedules. Can be possible.

  In situations where content or services will be sold through different channels, the profile and recommendation system can consist of multiple storefronts. For example, a mobile operator can market content or services through multiple brands or resellers. In one example, a customized storefront can be supported for each channel.

  The content module 1804 can be further configured to provide a reliable, audited mechanism for securing and managing content. In one example, security is provided via SSL and username / password authentication. According to one example, access to content can be isolated, thereby restricting content providers to access their content. Content review and authentication can be performed by the platform administrator 213 or by an external content owner.

  In one aspect, intelligent content selection can be used to allow the type of content offered by the provider to be delivered in an optimal format that matches the capabilities of the user or subscriber device. By mapping device capabilities to devices and content or service items, decisions regarding which services or content should be delivered can be made by the profile and recommendation system. If the device has several device capabilities, the profile and recommendation system can determine the most appropriate content to deliver using the weighting system.

  With continued reference to FIG. 22, in one example, data relating to the catalog module and profile module, 230 and 232, respectively, is communicated via a connection module 1802 to a system (eg, billing, CRM, value added service (VAS) platform (eg, Can be imported from the alert platform, etc)). In one aspect, the connection module 1802 provides a method for simplifying and automating the import and export of profiles and recommendation systems from the profile and recommendation system for the profile module 232 and the catalog module 230. .

  With reference to FIG. 23, an exemplary environment 1900 for implementing various aspects of the claimed subject matter includes a computer 1912. The computer 1912 includes a processing unit 1914, a system memory 1916, and a system bus 1918. System bus 1918 couples system components including, without limitation, system memory 1916 to processing unit 1914. The processing unit 1914 may be any of various available processors. Dual microprocessors and other multiprocessor architectures may also be employed as the processing unit 1914.

  The system bus 1918 includes, but is not limited to, Industrial Standard Architecture (ISA), Micro Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), peripheral component components Includes Connection (PCI), Card Bus, Universal Serial Bus (USB), Advanced Graphics Port (AGP), Personal Computer Memory Card International Association Bus (PCMCIA), FireWire (IEEE 1394), and Small Computer System Interface (SCSI) Memory bus or memory controller, peripheral or external bus, and / or local bus using various available bus architectures It may be any of several types of bus structures including a.

  The system memory 1916 includes volatile memory 1920 and non-volatile memory 1922. A basic input / output system (BIOS) includes basic routines for transferring information between elements within computer 1912, for example during startup, and is stored in non-volatile memory 1922. By way of example and not limitation, non-volatile memory 1922 may be read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash. Memory can be included. Volatile memory 1920 includes random access memory (RAM) that functions as external cache memory. By way of example and without limitation, RAM can be static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), sync link DRAM (SLDRAM). ), Rambus Direct RAM (RDRAM), Direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM).

  Computer 1912 also includes removable / non-removable, volatile / non-volatile computer storage media. FIG. 23 shows a disk storage 1924, for example. The disk storage 1924 includes devices such as, but not limited to, magnetic disk drives, floppy disk drives, tape drives, jazz drives, zip drives, LS-100 drives, flash memory cards, or memory sticks. Further, the disk storage 1924 may be, without limitation, a compact disk ROM device (CD-ROM), a CD recordable drive (CD-R drive), a CD rewritable drive (CD-RW drive) or a digital versatile disk. Storage media can be included separately or in combination with other storage media including optical disk drives such as ROM drives (DVD-ROM). For the purpose of facilitating connection of the disk storage device 1924 to the system bus 1918, a removable or non-removable interface such as interface 1926 is typically used.

  It should be understood that FIG. 23 illustrates software that acts as an intermediary between a user and basic computer resources as described in a suitable operating environment 1900. The software includes an operating system 1928. Operating system 1928 can be stored in disk storage 1924 and serves to control and allocate computer system 1912 resources. System application 1930 utilizes resource management by operating system 1928 through program modules 1932 and program data 1934 stored either in system memory 1916 or disk storage 1924. It is to be understood that the claimed subject matter can be implemented with various operating systems or combinations of operating systems.

  A user enters commands or information into computer 1912 through input device 1936. The input device 1936 includes, without limitation, a mouse, a trackball, a stylus, a touch pad, a keyboard, a microphone, a joystick, a game pad, a satellite antenna, a scanner, a TV tuner card, a digital camera, a digital video camera, a webcam, Etc. indicating device. These and other input devices connect to processing unit 1914 through system bus 1918 via interface port 1938. The interface port 1938 includes, for example, a serial port, a parallel port, a game port, and a universal serial bus (USB). The output device 1940 uses part of the same type of port as the input device 1936. Thus, for example, a USB port can be used to provide input to computer 1912 and to output information from computer 1912 to output device 1940. Output adapter 1942 is provided to indicate the presence of several output devices 1940 that require a special adapter, such as monitors, speakers, and printers among other output devices 1940. The output adapter 1942 includes, by way of example and not limitation, video and sound cards that provide a means of connection between the output device 1940 and the system bus 1918. It should be noted that other devices and / or systems of devices provide both input and output capabilities, such as remote computer 1944.

  Computer 1912 is capable of operating in a networked environment using logical connections to one or more remote computers, such as remote computer 1944. The remote computer 1944 can be a personal computer, server, router, network PC, workstation, microprocessor-based equipment, peer device or other common network node, etc., typically described with respect to the computer 1912. Including many or all of the elements to be performed. Only memory storage device 1946 is shown with remote computer 1944 for the sake of brevity. Remote computer 1944 is logically connected to computer 1912 through network interface 1948 and physically connected via communication connection 1950. Network interface 1948 includes wired and / or wireless communication networks, such as a local area network (LAN) and a wide area network (WAN). LAN technologies include fiber distributed data interface (FDDI), copper distributed data interface (CDDI), Ethernet, token ring, and the like. WAN technology includes, without limitation, point-to-point links, circuit switched networks such as integrated services digital networks (ISDN) and variants thereof, packet switched networks, and digital subscriber lines (DSL). .

  Communication connection 1950 refers to the hardware / software employed to connect network interface 1948 to bus 1918. Communication connection 1950 is shown in computer 1912 for purposes of clarity, but communication connection 1950 can also exist outside computer 1912. The hardware / software needed to connect to the network interface 1948 is for typical purposes only, internal and external technologies such as regular telephone grade modems, cable modems, DSL modems, ISDN A modem including an adapter and an Ethernet card.

  In FIG. 24, the network device 2400 is a computer comprising means for causing one or more processors 2404 to perform the methods described herein for profiling users of wireless devices and recommending content. A readable storage medium 2402 is included. The means may be a set of software code, firmware, hardware module implementation, or a combination thereof. Network communication module 2406 facilitates sending recommendations to the wireless device, which in an exemplary implementation is by communication with a mobile operator (not shown in FIG. 24). In one exemplary aspect, module 2408 accesses attribute data and behavior data for multiple users of corresponding multiple mobile devices. Module 2410 generates a recommendation for the content to be offered based on the attribute data, and generates a recommendation for the content to be offered based on the behavior data. Module 2412 selects a subset of recommendations by applying filtering restrictions. Module 2414 sends the subset of recommendations to at least a subset of the plurality of mobile devices.

In one example, the profile module and catalog module, 232 and 230, respectively, can provide an HTTP / XML based API for data transfer. These APIs (in collaboration with web-based UIs) can meet data exchange requirements for deployment (eg, early deployment, etc.). In one example, data exchanges that require more complex integration can also be supported. In one example, the connection module 1802 handles data exchange through the use of a data exchange agent that can provide a mechanism for importing and exporting content in different formats using a variety of different transport mechanisms. The connection module 1802 can also support an external system having its own data display means. For example, the external system may have specific requirements / capabilities regarding how the external system can deliver or receive data. In addition, external systems can use different transport mechanisms for online or offline (batch) transport. For example, hypertext transfer protocol (HTTP), simple object access protocol (SOAP), content-based retrieval architecture (COBRA), remote method invocation (RMI), etc. can be used for online mechanisms, and FTP for offline mechanisms. And message queues can be used. The transport layer of the connection module 1802 supports a plurality of configurable transports.

  In one example, the transport layer has the following responsibilities: That is, (A) Data extraction via the corresponding protocol. This can include retrieving and opening a file via FTP, or receiving XML encoded data via HTTP, (B) streaming data to a configured encoder. (C) Data archiving, where data can be passed to multiple encoder instances simultaneously to improve performance. Optionally, store the processed data for future reference, and (D) each transporter is associated with an encoder and uses the encoder to process one of the received data. It is possible to generate the above instances. Multiple transporters can be configured for each integration point.

  In one example, connection module 1802 may use an encoder to convert data from an external system format to a format acceptable by profile module 232 or catalog module 230, or vice versa. The encoder knows the implementation-specific data format and knows how to convert this format to the format required by the profile and recommendation system. In one example, the main responsibility of the encoder can be as follows. That is, (A) receive input from the transport agent, (B) check the validity of the received data, and generate an exception report if necessary. Exception reports include a record of data that is poorly formatted or incomplete. (C) In one aspect, it may be difficult to receive data that does not contain unnecessary or unwanted data. In the latter scenario, an encoder filter is used to determine the data elements to be discarded, (D) inserting, updating and deleting data from profile modules or catalog modules, 232 and 230, respectively, and ( E) Provide a detailed log of encoding activity. In one example, when executed, the encoder has full access to the data already contained in the profile module and catalog module, 232 and 230, respectively. This allows the encoder to inspect existing data before importing a new one.

  According to one aspect, the profile and recommendation system provides a constant default encoder for a common data format. New encoders that implement new or specific customer data formats can be easily developed. In one example, the encoder can be written in Java. This also allows new encoders to be written using a robust, high performance industry standard language familiar to customers or integrators.

  In one example, the profile and recommendation system portal API is a SOAP-based system that gives content provider websites, web portals, or other end-user systems access to profile and recommendation system profile and catalog information. is there. In one example, the portal API can be used for: That is, (A) providing a targeted promotional product (eg, a banner ad, etc.) defined by the promotion module 236, (B) from information held in the catalog module 230 via the portal. Add content (eg, wallpaper, ringtones, etc.) available, (C) provide search functionality from metadata held in the catalog module 230, (D) information held in the profile module 232 And (E) updating the profile and recommendation system with events that occur in the portal (eg, visits by subscribers, clicks on ads, purchases of content items, etc.).

  It should be understood that several developments of this disclosure utilize this disclosure primarily with respect to a central catalog combined with promotional and portal integration capabilities. In this scenario, it can be emphasized that the catalog module 230 is maintained with complete and up-to-date information about content and services. Promotion and decision modules, 236 and 234, respectively, can be utilized as much as possible to increase uptake. In this scenario, the integration of the portal is to extract from the catalog module 230 some or all of what the portal knows about the content or services available from the normal promotional aspects (banner ads, etc.). It is possible to expand to. Other developments can focus on utilizing this disclosure for promotional and distribution capabilities. The chosen solution depends on customer requirements and can evolve over time.

  In one or more aspects, the profile and recommendation system of this disclosure can be deployed on a common basic architecture that provides carrier-grade performance, reliability, and scalability. This architecture may also provide a consistent integration point for network distribution infrastructure, CRM, billing and other BSS systems. Furthermore, a common architecture can support multiple solutions built on different capabilities in a highly modular and configurable way.

  By way of example, the profile and recommendation system can be deployed on different hardware, including, without limitation, hardware running Solaris, HP-UX, Linux (registered trademark), and Windows (registered trademark). Including. In one aspect, the profile and recommendation system can be divided into three layers, each of which is deployed on shared or different hardware depending on the deployment criteria and performance requirements of the mobile operator. It is possible. In one example, an Oracle database can be used for data storage and data storage management.

  By way of example, the profile and recommendation system architecture can operate to provide the highest level of performance required by mass mobile operators. The decision module 234 provides a large amount of real-time recommendation generation from a database of extensive profile and catalog information. The promotion module 236 can distribute a large amount of online and outbound promotions, and the content module 1804 can manage and distribute a large amount of content.

  In one example, the profile and recommendation system can further provide redundant deployment, thereby ensuring that there is virtually no single point of failure. In this way, high availability of services that generate all revenue can be guaranteed. The profile and recommendation system 101 includes (A) a hot standby configuration that can transfer the functionality of each software component to a backup server, (B) use of a server with embedded redundant hardware components, (C ) Load balancer at all interface points, (D) Oracle9i database technology that provides access to high throughput and highly available databases, and (E) Simple Network Management Protocol (SNMP) monitoring and alerting, Simple Mail Transfer It can also provide carrier-grade reliability and availability through the use of protocol (SMTP) alerts, allowing integration with existing network management platforms To.

  The profile and recommendation system can further provide a powerful scalability option to meet customers' current and anticipated performance requirements by utilizing processing resources in a cost-effective and flexible manner. In one or more examples, all components of the architecture can be designed to be multi-threaded and to make maximum use of multiple CPU servers. Depending on the available resources, according to one aspect, the system can be appropriately configured to allow complete control over processing elements such as threads and database connections. In addition to providing scalability within the host, the profile and recommendation system can scale across several nodes, adding new nodes that provide a nearly linear improvement in system performance. Is possible.

  Further, according to one or more aspects, the profile and recommendation system can provide various APIs that allow the system to be easily integrated with other applications. In one non-limiting example, the API includes XML / SOAP, RMI, JDBC, etc. Many integration points that allow new or existing business logic to be inserted into the processing flow of different modules can also be provided.

  Further, by way of example, the profile and recommendation system can provide intuitive web-based management and provisioning of all aspects of platform behavior and system workflow. The system may further provide an interface for basic platform management and management of each module.

  In addition, the profile and recommendation system can provide SNMP integration with a management system such as HP Openview. The system can also provide detailed log files for all system components, and the log level can be set on a per component basis. In one or more aspects, the log level can be changed in real time.

  In addition, the profile and recommendation system includes various network connectivity protocols (eg, Short Message Peer to Peer Protocol (SMPP), Computer Interface Message Delivery (CIMD), Universal Computer Protocol (UCP), EAIF, MM7, MM1, Password Authentication Protocol). (PAP), and over-the-air (OTA) In one example, the platform can connect to an unlimited number of network distribution points simultaneously and perform complex content routing.

  It should be noted that the profile and recommendation system can be configured to support the delivery of content or services to users or subscribers of mobile operators having more than one mobile operator or several subsidiaries. .

  In addition, the profile and recommendation system supports access control in all external interfaces through the appropriate use of SSL, VPN, source address validation, and user / password validation applicable to the protocol in question, Network management can be provided by bandwidth control using content provider peak message output and security management.

  The profile and recommendation system can further be used to throttle the rate at which messages enter the system from applications and message sending centers. Furthermore, it can be used to ensure that certain application traffic is given priority.

  Further, according to one or more aspects, the profile and recommendation system may provide a reporting function that is responsible for capturing relevant system data for report generation or customer service queries. The system can obtain substantially all the details of the traffic required to generate a complete audit trail. Various reports are available through the administration website. In one aspect, the type of reporting available can depend on the solution being deployed. In one example, by default, different components may have various most commonly used built-in reports. However, the profile and recommendation system provides reports regarding additional specific customers to be generated. In one example, an embedded reporting tool can be used. This approach allows the use of advanced GUI tools to generate the desired custom reports and make the reports readily available from the profile and recommendation website. From the website, it is possible to browse summary information (in text or graphic form) and download detailed information in CSV format. In one example, the report can include a management dashboard that includes web-based interface statistics obtained from the use and promotion of all services and alert capabilities for preset triggers.

  The report can also include a real-time dashboard, which includes the current health of all servers, the current state of all active servers, trend information on the number of transactions per service, transaction volume by content provider It is possible to provide information such as trend information, top 10 current recommendations by subscriber type, responses for each recommendation, and the like.

  In accordance with one or more aspects, the profile and recommendation system of this disclosure can be used to proactively incorporate content or services by providing unified viewing, advanced profiling, and intelligent recommendations. It is possible to assist the operator with a complex combination of services and a diverse subscriber base. Its capabilities depend on current constraints in active retailing of all content or services across the mobile channel (eg how to promote content services from multiple disparate systems to each other, individual subscribers How to sell to these individual subscribers based on their demographic figures, available funds and usage patterns, services applicable to devices and subscribers without losing sales opportunities Matching can improve the user experience, how to automate targeted promotions, etc.), etc., to enable mobile operators to overcome.

  The disclosed profile and recommendation system, for example, (A) maximizes sales opportunities available to mobile operators, (B) automates the promotion of content and services with minimal overhead for staff, (C) Maximize existing investments in mobile operators' current content and data platforms, improve all relationships mobile operators have with content providers, (D) improve retention and generate subscriber commitments ( E) Increase data ARPU by aggressively pushing higher margins (eg, at least 3 times), and (F) By providing an end-to-end retail environment for mobile operators that reduce complexity Overcoming all the challenges mentioned above Bets are possible.

  Variations, changes, and other implementations of what is described herein will be made by those skilled in the art without departing from the spirit and scope of the claimed disclosure. Accordingly, the disclosure is to be defined not by the preceding illustrative description but instead by the spirit and scope of the following claims.

Claims (67)

  1. A method for generating recommendations for a user of a mobile device, wherein the mobile device is associated with a service provider;
    A request acquisition means to obtain a recommendation request;
    Data retrieval means retrieves data associated with the user and data regarding content available for the mobile device from the service provider;
    And that recommendation generation means, and generating a plurality of recommendations based on an analysis of the retrieved data, each of the plurality of recommendations is generated by a plurality of different recommendation techniques,
    Selecting means for selecting a subset of the generated plurality of recommendations based on filtering restrictions ; and
    Generating a plurality of recommendations by the recommendation generating means,
    Providing the retrieved data to each of the plurality of different recommendation techniques, each generated recommendation having an associated confidence level;
    Combining the recommendations from each of the recommendation techniques in order of confidence level .
  2.   The method of claim 1, further comprising delivering the generated recommendation to a user interface accessible by the user.
  3.   The method of claim 2, wherein the delivering the selected subset of recommendations further comprises delivering the selected subset of recommendations to a portal associated with the service provider.
  4.   The method of claim 2, wherein the delivering the generated recommendation to the user interface accessible by the user further comprises delivering the recommendation to a mobile device.
  5.   The method of claim 4, wherein the delivering to the mobile device is via a wireless application protocol (WAP) push message to a portal associated with the service provider.
  6.   The method of claim 4, wherein the delivering to the mobile device is via a short message service (SMS) message.
  7.   The method of claim 4, wherein the delivering to the mobile device is via a multimedia message transmission service (MMS) message displayed on the mobile device.
  8. Changing the order of the recommendations based on weights defined by the user;
    The method of claim 1, further comprising filtering the reordered recommendations.
  9. Receiving the recommendation request including specific restrictions;
    9. The method of claim 8, further comprising filtering the reordered recommendations by filtering according to the specific restriction.
  10.   The filtering of the reordered recommendations further comprises excluding recommendations that have been previously obtained by the user or have been presented to the user a certain number of times. Item 9. The method according to Item 8.
  11.   The method of claim 8, wherein the filtering of the reordered recommendations further comprises excluding recommendations that are not compatible with a mobile device.
  12.   The method of claim 1, further comprising normalizing the confidence level obtained from the different recommendation techniques.
  13.   The method of claim 1, wherein the plurality of recommendation techniques are selected from a group consisting of association recommenders, comparison recommenders, group classification recommenders, tracking recommenders, or network recommenders.
  14. The network recommender is:
    Selecting a plurality of people from a user list in the target user's local network, the plurality of people being within a specified degree of separation;
    Determining popular content that has been previously obtained by the selected plurality of people;
    The method of claim 13, further comprising generating a recommendation based on the determined service provider and popular content.
  15.   The method of claim 14, wherein selecting the plurality of people further comprises identifying the user associated with a high weighting value from the local network of the target user.
  16. The weight value is
    Retrieving one-to-one mobile communication data for the user from the service provider;
    Assigned by filtering the one-to-one mobile communication data to exclude unwanted communication data, and assigning a weighting value to each of the filtered one-to-one all communications, wherein the assigned value is: The method of claim 15, which is proportional to the quantity and type of one-to-one communication activity.
  17. The filtering of the one-to-one communication data includes:
    The method of claim 16, further comprising excluding communication data from undesired sources.
  18.   The method of claim 17, wherein the undesired source is identified by each unique telephone number.
  19.   The method of claim 16, wherein the filtering the one-to-one communication further comprises excluding communication data that is not desired due to a type or duration of communication.
  20.   The method of claim 16, wherein the filtering further comprises excluding communication data that is not desired due to communication time or date.
  21.   The one-to-one mobile communication data further comprises one or more of a voice call, a short message service (SMS) message, a multimedia message transmission service (MMS) message, or a mobile communication method. Method.
  22. The associated recommender is
    Establishing association rules from user behavior data retrieved from service providers;
    The method of claim 13, further comprising generating recommendations based on the established association rules.
  23. The comparative recommender is
    Building a link between similar content data available to the user using content metadata;
    The method of claim 13, further comprising generating a recommendation based on the constructed link.
  24. The tracking recommender is:
    Determining user activity history to establish a ranking of all content data, wherein the content data is ranked by popularity;
    14. The method of claim 13, comprising generating recommendations based on the ranking.
  25.   25. The method of claim 24, wherein the user activity comprises content purchases, ratings, or other indications of interest by the user over a configurable time period.
  26.   The data associated with the user includes call data, date of birth, gender, previous purchase, interest display, no interest display, spending pattern, mobile device type, current geographic location, call frequency The method of claim 1 comprising a selection of one or more of: or other user metadata.
  27.   27. The method of claim 26, further comprising maintaining the associated user data up to date when generating a recommendation.
  28.   The method of claim 1, wherein the request for recommendations is obtained from a portal associated with a service provider.
  29.   The method of claim 1, wherein the recommendation is generated in real time to avoid diverting the user experience.
  30.   The method of claim 1, wherein the recommendation is generated in a time less than 200 milliseconds.
  31. A method for generating a promotion for a user of a mobile device, wherein the user is associated with a service provider,
    The list obtaining means obtains a list of promotional items from the service provider;
    Data retrieval means retrieves data associated with a user and data relating to the content available to the user from the service provider;
    A recommendation list generating means generates a recommendation list for the user by analyzing the retrieved data, wherein the recommendation is generated by a plurality of individual recommendation techniques;
    Selecting means for selecting a subset of the retrieved promotions for distribution, the subset of the retrieved promotions being in common with the recommendations in the recommendation list; Including promotions and promotional materials not already obtained by the user ,
    Generating a recommendation list by the recommendation list generating means
    Providing the retrieved data to each of the plurality of different recommendation techniques, each generated recommendation having an associated confidence level;
    Combining the recommendations from each of the recommendation techniques in order of confidence level .
  32. A computer program,
    At least one instruction for causing the computer to obtain the request for recommendation;
    At least one instruction for causing the computer to retrieve data associated with a user and data relating to the content available to the user from the service provider;
    At least one instruction for causing the computer to generate a recommendation list based on an analysis of the retrieved data, wherein the recommendation is at least one generated by a plurality of different recommendation techniques. And two instructions
    At least one instruction for selecting a subset of the generated plurality of recommendations based on filtering restrictions;
    Generating the recommendation list includes:
    Providing the retrieved data to each of the plurality of different recommendation techniques, each generated recommendation having an associated confidence level;
    Combining the recommendations from each of the recommendation techniques in order of confidence level .
  33. A system for generating recommendations for a user of a mobile device, wherein the user is associated with a service provider,
    Means for obtaining recommendations for recommendations; and
    Means for retrieving data associated with a user and data relating to the content available to the user from a service provider;
    Means for generating a recommendation list based on an analysis of the retrieved data, wherein the recommendations are generated by a plurality of different recommendation techniques;
    Means for selecting a subset of the generated plurality of recommendations based on filtering restrictions ; and
    Means for generating the recommendation list include:
    Providing the retrieved data to each of the plurality of different recommendation techniques, each generated recommendation having an associated confidence level;
    Combining the recommendations from each of the recommendation techniques in order of confidence level .
  34. A system for generating recommendations for a user of a mobile device, wherein the user is associated with a service provider,
    A profile module for storing and processing data associated with the user;
    A catalog module for storing and processing content available to the user;
    A determination module in communication with the profile module and the catalog module, wherein the determination module is used to generate a recommendation list for the user by analysis of data retrieved from the profile module and the catalog module;
    A selection module for selecting a subset of the generated plurality of recommendations based on filtering restrictions, wherein the recommendations are generated by a plurality of individual recommender modules;
    Generating the recommendation list includes:
    Providing the retrieved data to each of the plurality of individual recommender modules, wherein each generated recommendation has an associated confidence level;
    Combining the recommendations from each of the plurality of individual recommender modules in order of confidence level .
  35.   35. The system of claim 34, wherein the determination module includes association recommenders, comparison recommenders, group classification recommenders, tracking recommenders, and network recommenders.
  36. The network recommender is:
    36. The system of claim 35, comprising a call data recording module, a network builder module, a network cleaning module, a weighting module, a relationship identification module, and a network recommender module.
  37.   35. The system of claim 34, wherein the profile module comprises a profile database module, a profile management module, a profile group classification module, and a profile capture module.
  38.   35. The system of claim 34, wherein the catalog module comprises a content group classification module, a search module, a content management module, a content database module, and a content capture module.
  39. A system for generating recommendations for a user of a mobile device, wherein the user is associated with a service provider,
    A profile module for storing and processing data associated with the user;
    A catalog module for storing and processing content available to the user;
    A decision module in communication with the profile module and the catalog module, wherein the recommendation module is used to generate a recommendation list for the user by analysis of data retrieved from the profile module and the catalog module; A decision module generated by a plurality of individual recommender modules;
    A promotion module for comparing the recommendation to the service provider's promotion database and for generating a list of promotions based on the comparison;
    The decision module for generating the recommendation list is:
    Providing the retrieved data to each of the plurality of individual recommender modules, wherein each generated recommendation has an associated confidence level;
    Combining the recommendations from each of the plurality of individual recommender modules in order of confidence level .
  40.   40. The system of claim 39, wherein the promotion module further comprises a promotion management module, a promotion feedback module, a promotion creation module, a promotion take-out module, and a promotion delivery module.
  41. A method for generating a recommendation for a user of a mobile device, comprising:
    The data access means accessing attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices;
    A recommendation generating means generates a recommendation for the content to be offered based on the attribute data, and generates a recommendation for the content to be offered based on the behavior data;
    The selection means selects a subset of recommendations by applying filtering restrictions; and
    Transmitting means for transmitting the subset recommendations to at least one subset of the plurality of mobile devices ;
    The selecting means applies a filtering restriction by determining a confidence level of a plurality of recommendations for the selected content item and selects a subset of recommendations by applying a weighting factor according to the confidence level. The method further comprising :
  42.   The method of claim 41, further comprising receiving a recommended user request for the selected content item.
  43.   42. The method of claim 41, further comprising applying a filtering restriction by accessing an exclusion restriction.
  44. Accessing the exclusion limit by tracking the number of offers of the selected content item for the selected user;
    44. The method of claim 43, further comprising: excluding further offers of the selected content item to the selected user in response to reaching a threshold.
  45.   44. The method of claim 43, further comprising accessing an exclusion restriction by receiving a category restriction for the selected user.
  46.   44. The method of claim 43, further comprising accessing the exclusion limit by determining that the selected content item has been previously selected and has been received by the selected user's selected mobile device. The method described.
  47.   The method of claim 46, further comprising identifying a content item for recommendation associated with the previously selected content item.
  48.   44. The method of claim 43, further comprising accessing an exclusion restriction by determining device suitability of the selected wireless device for the selected content item.
  49.   42. The method of claim 41, further comprising sorting the plurality of recommendations according to the confidence level.
  50.   42. The method of claim 41, further comprising immediately updating behavior data regarding the selected user of the wireless device based on interaction of the user with the presented recommendation offer.
  51.   42. The method of claim 41, further comprising associating a selected user of a wireless device with a user group and selecting a recommendation based on the association with the group.
  52.   Attributes consisting of call data, date of birth, gender, previous purchase, interest display, no interest display, spending pattern, mobile device type, current geographic location, call frequency, or other user metadata 52. The method of claim 51, further comprising accessing data and behavior data.
  53. At least one processor for generating a recommendation for a user of a mobile device, comprising:
    A first module for accessing attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices;
    A second module for generating a recommendation for the content to be offered based on the attribute data and generating a recommendation for the content to be offered based on the behavior data;
    A third module for selecting a subset of recommendations by applying filtering restrictions;
    A fourth module for transmitting the subset of recommendations to at least one subset of the plurality of mobile devices ;
    Further comprising applying a filtering restriction by determining a confidence level of a plurality of recommendations for the selected content item and selecting a subset of recommendations by applying a weighting factor according to the confidence level . At least one processor.
  54. A computer program in order to generate recommendations for the user of the mobile device,
    At least one instruction for causing a computer to access attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices;
    At least one instruction for causing the computer to generate a recommendation for the content to be offered based on the attribute data and to generate a recommendation for the content to be offered based on the behavior data When,
    At least one instruction for causing the computer to select a subset of recommendations by applying a filtering restriction;
    And at least one instruction for causing the computer to transmit the subset of recommendations to at least a subset of the plurality of mobile devices ;
    Further comprising applying a filtering restriction by determining a confidence level of a plurality of recommendations for the selected content item and selecting a subset of recommendations by applying a weighting factor according to the confidence level . Computer program.
  55. An apparatus for generating a recommendation for a user of a mobile device, comprising:
    Means for accessing attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices;
    Means for generating a recommendation for content to be offered based on the attribute data and generating a recommendation for content to be offered based on the behavior data;
    Means for selecting a subset of recommendations by applying filtering restrictions;
    Means for transmitting the subset of recommendations to at least one subset of the plurality of mobile devices ;
    Further comprising applying a filtering restriction by determining a confidence level of a plurality of recommendations for the selected content item and selecting a subset of recommendations by applying a weighting factor according to the confidence level . apparatus.
  56. An apparatus for generating a recommendation for a user of a mobile device, comprising:
    A profile storage component including attribute data and behavior data for a plurality of users of a corresponding plurality of mobile devices;
    By generating recommendations for the content to be offered based on the accessed attribute data, generating recommendations for the content to be offered based on the accessed behavior data, and applying filtering restrictions A profile and recommendation system for selecting a subset of recommendations;
    A network communication module for transmitting the subset of recommendations to at least one subset of the plurality of mobile devices ;
    Applying the filtering restriction by determining a confidence level of a plurality of recommendations for the selected content item, and selecting the subset recommendation by applying a weighting factor according to the confidence level; and An apparatus further comprising a recommendation system .
  57.   57. The apparatus of claim 56, further comprising the network communication module for receiving a recommended user request for a selected content item.
  58.   58. The apparatus of claim 57, further comprising the profile and recommendation system to apply a filtering restriction by accessing an exclusion restriction.
  59. Limit exclusion by tracking the number of offers of selected content items to selected users and excluding further offers of the selected items to the selected users in response to reaching a threshold 59. The apparatus of claim 58, further comprising the profile and recommendation system for accessing a device.
  60.   59. The apparatus of claim 58, further comprising the profile and recommendation system for accessing exclusion restrictions by receiving a category restriction for a selected user.
  61.   The profile and recommendation to access exclusion restrictions by determining that the selected content item has been previously selected and has been received by the selected mobile device of the selected user. 59. The apparatus of claim 58, further comprising a system.
  62.   62. The apparatus of claim 61, further comprising the profile and recommendation system to identify a content item for recommendation associated with the previously selected content item.
  63.   59. The apparatus of claim 58, further comprising the profile and recommendation system to access exclusion restrictions by determining device suitability of a selected wireless device with respect to a selected content item.
  64.   57. The apparatus of claim 56, further comprising the profile and recommendation system for sorting the plurality of recommendations according to the weighting factor.
  65.   57. The apparatus of claim 56, further comprising the profile and recommendation system to immediately update behavioral data for a selected user of a wireless device based on interaction with a user and a suggested recommendation offer.
  66.   57. The apparatus of claim 56, further comprising the profile and recommendation system for associating a selected user of a wireless device with a user group and selecting a recommendation based on the group association.
  67.   Attributes consisting of call data, date of birth, gender, previous purchase, interest display, no interest display, spending pattern, mobile device type, current geographic location, call frequency, or other user metadata The apparatus of claim 56, further comprising the profile and recommendation system for accessing data and behavioral data.
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